OCTDL: Optical Coherence Tomography Dataset for Image-Based Deep Learning Methods - Kulyabin, Mikhail; Zhdanov, Aleksei; Nikiforova, Anastasia; Stepichev, Andrey; Kuznetsova, Anna; Ronkin, Mikhail; Borisov, Vasilii; Bogachev, Alexander; Korotkich, Sergey; Constable, Paul; Maier, Andreas (Scientific Data, 2024)
Optical coherence tomography (OCT) is a non-invasive imaging technique with extensive clinical applications in ophthalmology. OCT enables the visualization of the retinal layers, playing a vital role in the early detection and monitoring of retinal diseases. OCT uses the principle of light wave interference to create detailed images of the retinal microstructures, making it a valuable tool for diagnosing ocular conditions. This work presents an open-access OCT dataset (OCTDL) comprising over 2000 OCT images labeled according to disease group and retinal pathology. The dataset consists of OCT records of patients with Age-related Macular Degeneration (AMD), Diabetic Macular Edema (DME), Epiretinal Membrane (ERM), Retinal Artery Occlusion (RAO), Retinal Vein Occlusion (RVO), and Vitreomacular Interface Disease (VID). The images were acquired with an Optovue Avanti RTVue XR using raster scanning protocols with dynamic scan length and image resolution. Each retinal b-scan was acquired by centering on the fovea and interpreted and cataloged by an experienced retinal specialist. In this work, we applied Deep Learning classification techniques to this new open-access datase

OCT-OCTA Segmentation: a Novel Framework and an Application to Segment Bruch's Membrane in the Presence of Drusen - Schottenhamml, Julia; Moult, Eric M.; Novais, Eduardo A.; Kraus, Martin; Lee, Byungkun; Choi, Woojhon; Ploner, Stefan; Husvogt, Lennart; Lu, Chen D.; Yiu, Patrick; Rosenfeld, Philip; Duker, Jay S.; et al. (Investigative Ophthalmology & Visual Science, 2017)
Purpose : We present a novel framework for segmenting optical coherence tomography (OCT) and OCT angiography (OCTA) that jointly uses structural and angiographic information. We term this new paradigm “OCT-OCTA segmentation,” and demonstrate its utility by segmenting Bruch’s membrane (BM) in the presence of drusen. Methods : We developed an automatic OCT-OCTA graph-cut algorithm for BM segmentation. Our algorithm’s performance was quantitatively validated by comparing it with manual segmentation in 7 eyes (6 patients; 73.8±5.7 y/o) with drusen. The algorithm was also qualitatively assessed in healthy eyes (n=13), eyes with diabetic retinopathy (n=21), early/intermediate age-related macular degeneration (AMD) (n=14), exudative AMD (n=5), geographic atrophy (GA) (n=6), and polypoidal choroidal vasculopathy (n=7). Results : The absolute pixel-wise error between the manual and automatic segmentations had the following values: mean: 4.5±0.89um; 1st Quartile: 1.9±1.35um; 2nd Quartile: 3.9±1.90um; and 3rd Quartile: 6.3±2.67. This corresponds to a mean absolute error smaller than the optical axial resolution of our OCT system ( 8-9um). In all other tested eyes, qualitative visual inspection showed BM contours that were deemed suitably accurate for use in forming en face OCT(A) projections. The algorithm’s poorest results occurred in GA patients with large areas of atrophy. Conclusions : By leveraging both structural and angiographic information we showed that OCT-OCTA segmentation is likely to be a widely useful framework for segmenting ocular structur
UnivIS-Import:2018-09-11:Pub.2017.tech.IMMD.IMMD5.octoct_8
Keywords: oct; octa; oct angiography; layer segmentation; drusen; diabetic retinopathy

OCT-OCTA Segmentation - Schottenhamml, Julia; Moult, Eric M.; Novais, Eduardo A.; Kraus, Martin; Lee, Byungkun; Choi, WooJhon; Ploner, Stefan; Husvogt, Lennart; Lu, Chen D.; Yiu, Patrick; Rosenfeld, Philip; Duker, Jay S.; et al. (Bildverarbeitung für die Medizin 2018, 2018)
In this work, a novel paradigm for segmenting optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA) is presented [1]. Since it uses OCT and OCTA information jointly it is called “OCT-OCTA segmentation” and its usefulness is demonstrated by segmenting the Bruch’s Membrane (BM) in the presence of drusen. Therefore a fully automatic graph-cut algorithm was developed and evaluated by comparing the automatic segmentation results with manual segmentation in 7 eyes (6 patients; 73.8 ± 5.7 y/o) with nascent geographic atrophy and/or drusen associated geographic atrophy.<br
UnivIS-Import:2018-09-11:Pub.2018.tech.IMMD.IMMD5.octoct

OCT-OCTA segmentation: Combining structural and blood flow information to segment bruch's membrane - Schottenhamml, Julia; Moult, Eric M.; Ploner, Stefan; Chen, Siyu; Novais, Eduardo; Husvogt, Lennart; Duker, Jay S.; Waheed, Nadia K.; Fujimoto, James G.; Maier, Andreas (Biomedical Optics Express, 2021)
In this paper we present a fully automated graph-based segmentation algorithm that jointly uses optical coherence tomography (OCT) and OCT angiography (OCTA) data to segment Bruch's membrane (BM). This is especially valuable in cases where the spatial correlation between BM, which is usually not visible on OCT scans, and the retinal pigment epithelium (RPE), which is often used as a surrogate for segmenting BM, is distorted by pathology. We validated the performance of our proposed algorithm against manual segmentation in a total of 18 eyes from healthy controls and patients with diabetic retinopathy (DR), non-exudative age-related macular degeneration (AMD) (early/intermediate AMD, nascent geographic atrophy (nGA) and drusen-associated geographic atrophy (DAGA) and geographic atrophy (GA)), and choroidal neovascularization (CNV) with a mean absolute error of ∼0.91 pixel (∼4.1 µm). This paper suggests that OCT-OCTA segmentation may be a useful framework to complement the growing usage of OCTA in ophthalmic research and clinical communities.
CRIS-Team Scopus Importer:2021-01-08

Odin’s Eye – A Close Look at Gamification of Labelling of Ophthalmic Diseases - Fan, Fuxin; Qiu, Jingna; Wang, JiaWei; Valianos, Stelica; Farooq, Muhammad Muqtasid; Fu, Weilin; Kordon, Florian Johannes; Maier, Andreas (Joint Conference of the GMDS & CEN-IBS 2020, 2020)
Introduction State of the art deep learning [1] requires vast amounts of accurately labeled training data to enable high classification performance [2]. In order to obtain sufficient amounts of data, data donation is a feasible approach [3]. Yet, the data is only usable, if correct annotations are present. One way to create such annotations is crowd-sourcing via gamification. In this paper, we present an integrated training and annotation approach that allows large scale annotation of ophthalmic diseases. Methods We created a game called “Odin’s Eye” in order to make image classification an exciting and rewarding experience. The game has three modes that are used to slowly lead the player to the complex field of ophthalmic diseases. In order to do so, we used image data from the ODIR 2019 Challenge (https://odir2019.grand-challenge.org/dataset/). The dataset contains more than 7000 images showing healthy eye data and different pathologies. In “Normal Mode”, the player is shown one fundus image and is asked to select whether the shown image is “normal” or shows Cataract, Macular Degeneration, Glaucoma, Retinopathy, or Myopia. After a sufficient number of correct answers, the player can access the unlabeled mode, in which the user is able to give annotation to unlabeled image. The annotations for the unlabeled images will be shown in the “Unlabeled Result”. The “Difficult Mode” is inspired by puzzle games such as Candy Crush or Zookeeper. Here, the player is displayed various fundus images and is asked to align them such that three images showing the same pathology form either a row or a column. Once such a triplet is found, it is eliminated and new images enter the game canvas from the top. In order to make the game more challenging, a curtain enters the field of view from the top that gradually increases the pressure on the player as well as increases game difficulty via decreasing the image brightness. After each successful elimination, the curtain is raised a little and the time limit is increased. Rewarding strategies like reward animation and image shuffling are applied to achieve better user experience. The idea of using triplets is appealing as images of unknown pathology can be mixed in the game area. The player will implicitly classify such images when he tries to align them with two more images of the same class. A leader board shows high scores in order to encourage players to compare their scores online and to compete in creating a large number of annotations. Results A prototype was implemented in Unity [4]. In order to test the game idea, only five prototypical images were chosen for each pathology at present. The difficulty of the game can easily be increased by increasing types of diseases. First experiences with test players confirmed that in particular the “Difficult Mode” has a very rewarding game experience. Prototypes are available as Android APK and WebGL (https://www.medicaldatadonors.org/index.php/odins-eye/). An in-game video was created to demonstrate the gameplay (https://youtu.be/UehnND9gkvY). Conclusion A believe that we created a challenging yet rewarding game experience. In future versions, we will make use of additional images from the ODIR 2019 dataset in order to create slowly increasing difficulty in the game to convince even expert players to keep playing Odin’s Eye. References [1] Maier, A., Syben, C., Lasser, T., & Riess, C. (2019). A gentle introduction to deep learning in medical image processing. Zeitschrift für Medizinische Physik, 29(2), 86-101. [2] Bertram, C. A., Aubreville, M., Marzahl, C., Maier, A., & Klopfleisch, R. (2019). A large-scale dataset for mitotic figure assessment on whole slide images of canine cutaneous mast cell tumor. Scientific data, 6(1), 1-9. [3] Servadei, L., Schmidt, R., Eidelloth, C., & Maier, A. (2017, October). Medical Monkeys: A Crowdsourcing Approach to Medical Big Data. In OTM Confederated International Conferences" On the Move to Meaningful Internet Systems" (pp. 87-97). Springer, Cham. [4] Murray, J. W. (2014). C# game programming cookbook for Unity 3D. AK Peters/CRC Press
Keywords: Gamification; Medical Image Annotation; Deep Learning

ODOR: The ICPR2022 ODeuropa Challenge on Olfactory Object Recognition - Zinnen, Mathias; Madhu, Prathmesh; Kosti, Ronak Vijaypal; Bell, Peter; Maier, Andreas; Christlein, Vincent (2022 26TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR), 2022)
CRIS-Team WoS Importer:2023-02-17

Offer Proprietary Algorithms Still Protection of Intellectual Property in the Age of Machine Learning?: A Case Study Using Dual Energy CT Data - Maier, Andreas; Yang, Seung Hee; Maleki, Farhad; Muthukrishnan, Nikesh; Forghani, Reza (Informatik aktuell, 2022)
In the domain of medical image processing, medical device manufacturers protect their intellectual property in many cases by shipping only compiled software, i.e. binary code which can be executed but is difficult to be understood by a potential attacker. In this paper, we investigate how well this procedure is able to protect image processing algorithms. In particular, we investigate whether the computation of mono-energetic images and iodine maps from dual energy CT data can be reverse-engineered by machine learning methods. Our results indicate that both can be approximated using only one single slice image as training data at a very high accuracy with structural similarity greater than 0.98 in all investigated cases.<br
CRIS-Team Scopus Importer:2022-05-06

Offline Writer Identification Using Convolutional Neural Network Activation Features - Christlein, Vincent; Bernecker, David; Maier, Andreas; Angelopoulou, Elli (Pattern Recognition, 2015)
Convolutional neural networks (CNNs) have recently become the state-of-the-art tool for large-scale image classification. In this work we propose the use of activation features from CNNs as local descriptors for writer identification. A global descriptor is then formed by means of GMM supervector encoding, which is further improved by normalization with the KL-Kernel. We evaluate our method on two publicly available datasets: the ICDAR 2013 benchmark database and the CVL dataset. While we perform comparably to the state of the art on CVL, our proposed method yields about 0.21 absolute improvement in terms of mAP on the challenging bilingual ICDAR dataset.
UnivIS-Import:2016-02-10:Pub.2015.tech.IMMD.IMMD5.offlin_86

On C-arm CT imaging with the extended line-ellipse-line trajectory - Guo, Z.; Lauritsch, G.; Maier, Andreas; Kugler, P.; Islam, M.; Vogt, F.; Noo, F. (2019 IEEE NUCLEAR SCIENCE SYMPOSIUM AND MEDICAL IMAGING CONFERENCE (NSS/MIC), 2019)
CRIS-Team WoS Importer:2020-10-16

One-Shot Object Detection in Heterogeneous Artwork Datasets - Madhu, Prathmesh; Meyer, Anna; Zinnen, Mathias; Mührenberg, Lara; Suckow, Dirk; Bendschus, Torsten; Reinhardt, Corinna; Bell, Peter; Verstegen, Ute; Kosti, Ronak Vijaypal; Maier, Andreas; Christlein, Vincent (2022 Eleventh International Conference on Image Processing Theory, Tools and Applications (IPTA), 2022)
Christian archeologists face many challenges in understanding visual narration through artwork images. This understanding is essential to access underlying semantic in-formation. Therefore, narrative elements (objects) need to be labeled, compared, and contextualized by experts, which takes an enormous amount of time and effort. Our work aims to reduce labeling costs by using one-shot object detection to generate a labeled database from unannotated images. Novel object categories can be defined broadly and annotated using visual examples of narrative elements without training exclusively for such objects. In this work, we propose two ways of using contextual information as data augmentation to improve the detection performance. Furthermore, we introduce a multi-relation detector to our framework, which extracts global, local, and patch-based relations of the image. Additionally, we evaluate the use of contrastive learning. We use data from Christian archeology (CHA) and art history - IconArt-v2 (IA). Our context encoding approach improves the typical fine-tuning approach in terms of mean average precision (mAP) by about 3.5 % (4 %) at 0.25 intersection over union (IoU) for UnSeen categories, and 6 % (1.5 %) for Seen categories in CHA (IA). To the best of our knowledge, our work is the first to explore few shot object detection on heterogeneous artistic data by investigating evaluation methods and data augmentation strategies. We will release the code and models after acceptance of the wor
Keywords: one-shot; object detection; digital humanities; computational humanities; data augmentation

On Mathews Correlation Coefficient and Improved Distance Map Loss for Automatic Glacier Calving Front Segmentation in SAR Imagery - Davari, Amirabbas; Islam, Saahil; Seehaus, Thorsten; Hartmann, Andreas; Braun, Matthias; Maier, Andreas; Christlein, Vincent (IEEE Transactions on Geoscience and Remote Sensing, 2021)
The vast majority of the outlet glaciers and ice streams of the polar ice sheets end in the ocean. Ice mass loss via calving of the glaciers into the ocean has increased over the last few decades. Information on the temporal variability of the calving front position (CFP) provides fundamental information on the state of the glacier and ice stream, which can be exploited as calibration and validation data to enhance ice dynamics modeling. To identify the CFP automatically, deep neural network-based semantic segmentation pipelines can be used to delineate the acquired synthetic aperture radar (SAR) imagery. However, the extreme class imbalance is highly challenging for the accurate calving front segmentation in these images. Therefore, we propose the use of the Mathews correlation coefficient (MCC) as an early stopping criterion because of its symmetrical properties and its invariance toward class imbalance. Moreover, we propose an improvement to the distance map-based binary cross-entropy (BCE) loss function. The distance map adds context to the loss function about the important regions for segmentation and helps to account for the imbalanced data. Using MCC as early stopping demonstrates an average 2.16% dice coefficient improvement for the segmentation of the slightly imbalanced ice/nonice regions and 17.43% dice coefficient improvement for segmenting the highly imbalanced CFPs, compared to the commonly used BCE. The modified distance map loss further improves the calving front segmentation performance by another 1.6%. These results are encouraging as they support the effectiveness of the proposed methods for segmentation problems suffering from extreme class imbalances.
Keywords: Image segmentation; Ice; Radar polarimetry; Measurement; Monitoring; Task analysis; Optical losses; Class imbalance; improved distance map loss; Mathews correlation coefficient (MCC); semantic segmentation

On Providing Multi-Level Quality of Service for Operating Rooms of the Future. - Facco Rodrigues, Vinicius; da Rosa Righi, Rodrigo; Andre da Costa, Cristiano; Eskofier, Björn; Maier, Andreas (Sensors, 2019)
The Operating Room (OR) plays an important role in delivering vital medical services to patients in hospitals. Such environments contain several medical devices, equipment, and systems producing valuable information which might be combined for biomedical and surgical workflow analysis. Considering the sensibility of data from sensors in the OR, independently of processing and network loads, the middleware that provides data from these sensors have to respect applications quality of service (QoS) demands. In an OR middleware, there are two main bottlenecks that might suffer QoS problems and, consequently, impact directly in user experience: (i) simultaneous user applications connecting the middleware; and (ii) a high number of sensors generating information from the environment. Currently, many middlewares that support QoS have been proposed by many fields; however, to the best of our knowledge, there is no research on this topic or the OR environment. OR environments are characterized by being crowded by persons and equipment, some of them of specific use in such environments, as mobile x-ray machines. Therefore, this article proposes QualiCare, an adaptable middleware model to provide multi-level QoS, improve user experience, and increase hardware utilization to middlewares in OR environments. Our main contributions are a middleware model and an orchestration engine in charge of changing the middleware behavior to guarantee performance. Results demonstrate that adapting middleware parameters on demand reduces network usage and improves resource consumption maintaining data provisioning.

On rotation invariance in copy-move forgery detection - Christlein, Vincent; Riess, Christian; Angelopoulou, Elli (2010 IEEE International Workshop on Information Forensics and Security, 2010)

On the Accuracy of a Video-Based Drill-Guidance Solution for Orthopedic and Trauma Surgery: Preliminary Results - Magaraggia, Jessica; Kleinszig, Gerhard; Wei, Wei; Weiten, Markus; Graumann, Rainer; Angelopoulou, Elli; Hornegger, Joachim (SPIE Medical Imaging 2014, 2014)
Over the last years, several methods have been proposed to guide the physician during reduction and fixation of bone fractures. Available solutions often use bulky instrumentation inside the operating room (OR). The latter ones usually consist of a stereo camera, placed outside the operative field, and optical markers directly attached to both the patient and the surgical instrumentation, held by the surgeon. Recently proposed techniques try to reduce the required additional instrumentation as well as the radiation exposure to both patient and physician. In this paper, we present the adaptation and the first implementation of our recently proposed video camera-based solution for screw fixation guidance. Based on the simulations conducted in our previous work, we mounted a small camera on a drill in order to recover its tip position and axis orientation w.r.t our custom-made drill sleeve with attached markers. Since drill-position accuracy is critical, we thoroughly evaluated the accuracy of our implementation. We used an optical tracking system for ground truth data collection. For this purpose, we built a custom plate reference system and attached reflective markers to both the instrument and the plate. Free drilling was then performed 19 times. The position of the drill axis was continuously recovered using both our video camera solution and the tracking system for comparison. The recorded data covered targeting, perforation of the surface bone by the drill bit and bone drilling. The orientation of the instrument axis and the position of the instrument tip were recovered with an accuracy of 1:60 ± 1:22° and 2:03 ± 1:36 mm respectively. © 2014 SPIE.
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.onthea_7
Keywords: Video-guided method; Real-time guidance; Orthopedic and Trauma Surgery; Drill Sleeve

On the Automatic Classification of Reading Disorders - Maier, Andreas; Schafflhuber, Caroline; Bocklet, Tobias; Hönig, Florian Thomas; Kratz, Oliver; Horndasch, Stefanie; Nöth, Elmar; Moll, Gunther (Pattern Recognition in Information Systems, 2009)

On the Benefit of Dual-Domain Denoising in a Self-Supervised Low-Dose CT Setting - Wagner, Fabian; Thies, Mareike; Pfaff, Laura; Aust, Oliver; Pechmann, Sabrina; Weidner, Daniela; Maul, Noah; Rohleder, Maximilian; Gu, Mingxuan; Utz, Jonas; Denzinger, Felix; Maier, Andreas (Proceedings - International Symposium on Biomedical Imaging, 2023)
Computed tomography (CT) is routinely used for three-dimensional non-invasive imaging. Numerous data-driven image denoising algorithms were proposed to restore image quality in low-dose acquisitions. However, considerably less research investigates methods already intervening in the raw detector data due to limited access to suitable projection data or correct reconstruction algorithms. In this work, we present an end-to-end trainable CT reconstruction pipeline that contains denoising operators in both the projection and the image domain and that are optimized simultaneously without requiring ground-truth high-dose CT data. Our experiments demonstrate that including an additional projection denoising operator improved the overall denoising performance by 82.4-94.1 %/12.5-41.7 % (PSNR/SSIM) on abdomen CT and 1.5-2.9 %/0.4-0.5 % (PSNR/SSIM) on XRM data relative to the low-dose baseline. We make our entire helical CT reconstruction framework publicly available that contains a raw projection rebinning step to render helical projection data suitable for differentiable fan-beam reconstruction operators and end-to-end learning.
CRIS-Team Scopus Importer:2023-10-06
Keywords: Known Operator Learning; Low-Dose CT; Self-Supervised Denoising

On the Characteristics of Helical 3-D X-ray Dark-field Imaging - Felsner, Lina; Hu, Shiyang; Ludwig, Veronika; Anton, Gisela; Maier, Andreas; Rieß, Christian (Bildverarbeitung für die Medizin (BVM 2019), 2019)

On the Influence of Acquisition Angle and Slice Thickness on the in-plane Spatial Resolution of Calcifications in Digital Breast Tomosynthesis - Luckner, Christoph; Schebesch, Frank; Syben-Leisner, Christopher; Mertelmeier, Thomas; Maier, Andreas; Ritschl, Ludwig (Proceedings of the Fifth International Conference on Image Formation in X-Ray Computed Tomography, 2018)
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.onthei_5

On the Value of the Non-negativity Constraint in CT - Haase, Viktor; Hahn, Katharina; Schoendube, Harald; Stierstorfer, Karl; Maier, Andreas; Noo, Frederic (2019 IEEE NUCLEAR SCIENCE SYMPOSIUM AND MEDICAL IMAGING CONFERENCE (NSS/MIC), 2019)
Model based iterative reconstruction (MBIR) has attracted a lot of attention in X-ray computed tomography (CT). A strength of MBIR over classical filtered backprojection is its ability to apply constraints over the voxel values, which can be critical to improve image quality. Given that the linear attenuation coefficient of X-rays is non-negative, applying a non-negativity constraint appears very natural. Indeed, most MBIR-related publications in CT invoke it. However, there is little to no information in the literature on the intrinsic value of the non-negativity constraint. In this work, we shed light on this question in the context of two challenging imaging scenarios: (i) heavy truncation, (ii) photon starvation due to a metal implant. Real CT data sets are used, and the effect of the constraint is examined in terms of image similarity and closeness to a preferred ground truth. Additionally, convergence properties are examined. The reconstruction is performed using a provably converging algorithm applied with a large number of iterations to nearly reach convergence, and is also performed using ordered subsets to obtain a result in a manner that is more practical for clinical routine applications. Our results show that the non-negativity constraint can be both beneficial and detrimental depending on the imaging scenario. However, the observed differences tend to be much smaller than the overall level of inaccuracy in the image. We also find that the non-negativity constraint can prevent divergence when using ordered subsets, but this gain does not translate into a satisfactory reconstruction. Altogether, we conclude that strong value for the non-negativity constraint is difficult to demonstrate. This constraint could thus be discarded in favor of other constraints or utilization of algorithms that cannot handle it.<br
CRIS-Team WoS Importer:2020-10-16
Keywords: X-ray Computed Tomography; Model Based Iterative Reconstruction; Penalized Weighted Least-Squares; Non-Negativity Constraint

On the Value of the Non-negativity Constraint in CT - Haase, Viktor; Hahn, Katharina; Schoendube, Harald; Stierstorfer, Karl; Maier, Andreas; Noo, Frédéric (2019 IEEE Nuclear Science Symposium and Medical Imaging Conference Proceedings (NSS/MIC), 2019)
Model based iterative reconstruction (MBIR) has attracted a lot of attention in X-ray computed tomography (CT). A strength of MBIR over classical filtered backprojection is its ability to apply constraints over the voxel values, which can be critical to improve image quality. Given that the linear attenuation coefficient of X-rays is non-negative, applying a non-negativity constraint appears very natural. Indeed, most MBIR-related publications in CT invoke it. However, there is little to no information in the literature on the intrinsic value of the non-negativity constraint. In this work, we shed light on this question in the context of two challenging imaging scenarios: (i) heavy truncation, (ii) photon starvation due to a metal implant. Real CT data sets are used, and the effect of the constraint is examined in terms of image similarity and closeness to a preferred ground truth. Additionally, convergence properties are examined. The reconstruction is performed using a provably converging algorithm applied with a large number of iterations to nearly reach convergence, and is also performed using ordered subsets to obtain a result in a manner that is more practical for clinical routine applications. Our results show that the non-negativity constraint can be both beneficial and detrimental depending on the imaging scenario. However, the observed differences tend to be much smaller than the overall level of inaccuracy in the image. We also find that the non-negativity constraint can prevent divergence when using ordered subsets, but this gain does not translate into a satisfactory reconstruction. Altogether, we conclude that strong value for the non-negativity constraint is difficult to demonstrate. This constraint could thus be discarded in favor of other constraints or utilization of algorithms that cannot handle it.<br

OpenCL: A viable solution for high-performance medical image reconstruction? - Siegl, Christian; Hofmann, Hannes; Keck, Benjamin; Prümmer, Marcus; Hornegger, Joachim (Medical Imaging 2011: Physics of Medical Imaging, 2011)
Reconstruction of 3-D volumetric data from C-arm CT projections is a computationally demanding task. For interventional image reconstruction, hardware optimization is mandatory. Manufacturers of medical equipment use a variety of high-performance computing (HPC) platforms, like FPGAs, graphics cards, or multi-core CPUs. A problem of this diversity is that many different frameworks and (vendor-specific) programming languages are used. Furthermore, it is costly to switch the platform, since the code has to be re-written, verified, and optimized. OpenCL, a relatively new industry standard for HPC, promises to enable portable code. Its key idea is to abstract hardware in a way that allows an efficient mapping onto real CPUs, GPUs, and other hardware. The code is compiled for the actual target by the device driver. In this work we investigated the suitability of OpenCL as a tool to write portable code that runs efficiently across different hardware. The problems chosen are back- and forward-projection, the most time-consuming parts of (iterative) reconstruction. We present results on three platforms, a multi-core CPU system and two GPUs, and compare them against manually optimized native implementations. We found that OpenCL allows to share a common framework in one language across platforms. However, considering differences in the underlying architecture, a hardware-oblivious implementation cannot be expected to deliver maximal performance. By optimizing the OpenCL code for the specific hardware we reached over 90% of native performance for both problems, back- and forward-projection, on all platforms. ̈ 2011 SPIE.

Open-source 4D Statistical Shape Model of the Heart for X-ray Projection Imaging - Unberath, Mathias; Maier, Andreas; Fleischmann, Dominik; Hornegger, Joachim; Fahrig, Rebecca (Proceedings of the ISBI, 2015)
We present an open-source 4D (3D+t) statistical shape model of the heart developed as numerical phantom for cone-beam CT simulation. The training set consists of surface meshes from 20 ten-phase-CT angiography data sets extracted using automatic registration-based segmentation. Incorporating 90% of the training set variation, the model exhibits a generalization ability of 5.00 ± 0.93 mm and specificity of 7.30 ± 0.97 mm. The model is implemented in CONRAD, an open-source simulation and reconstruction framework. We provide all algorithms and high resolution projection data on the project's homepage. To the best of our knowledge, this is the first open-source 4D statistical shape model of the heart.
UnivIS-Import:2017-12-18:Pub.2015.tech.IMMD.IMMD5.openso
Keywords: cone-beam; CT; numerical phantom; open-source; Simulation; XCAT

Open source simulation of fixational eye drift motion in oct scans: Towards better comparability and accuracy in retrospective OCT motion correction - Nau, Merlin A.; Nau, Merlin; Moult, Eric M.; Fujimoto, James G.; Maier, Andreas (Informatik aktuell, 2020)
Point-wise scanning modalities like Optical Coherence Tomography (OCT) or Scanning Laser Ophthalmoscopy suffer from distortions due to the perpetual motion of the eye. While various motion correction approaches have been proposed, the absence of ground truth displacements or images entails a lack of accurate and comparable evaluations. The purpose of this paper is to close this gap by initiating an open source framework for the simulation of realistic eye motion and corresponding artificial distortion of scans, thereby for the first time enabling the community to a) create datasets with accessible ground truth and b) compare the correction of identical motion patterns in data acquired with different scanners or scan patterns. This paper extends previous work on simulation of fixational eye drift via a self-avoiding random walk in a potential to a continuous domain in time and space, allowing the derivation of smooth displacement fields. The model is demonstrated by presenting an examplary motion path, whose properties resemble reported properties of recordings in current literature on fixational eye motion. Furthermore, the artificial distortion of scans is demonstrated by showing a correspondingly distorted image of a virtual raster scan modeled according to the properties of an existing OCT scanner. All experiments can be reproduced and adapted to arbitrary scanner- and raster scan pattern-properties in the publicly available framework. Beyond that, the open source code provides a starting point for the community to integrate extensions like saccadic or axial eye motion.
CRIS-Team Scopus Importer:2020-04-21

Open-source workflow approaches to passive acoustic monitoring of bats - Brinkløv, Signe M.M.; Macaulay, Jamie; Bergler, Christian; Tougaard, Jakob; Beedholm, Kristian; Elmeros, Morten; Madsen, Peter Teglberg (Methods in Ecology and Evolution, 2023)
The affordability, storage and power capacity of compact modern recording hardware have evolved passive acoustic monitoring (PAM) of animals and soundscapes into a non-invasive, cost-effective tool for research and ecological management particularly useful for bats and toothed whales that orient and forage using ultrasonic echolocation. The use of PAM at large scales hinges on effective automated detectors and species classifiers which, combined with distance sampling approaches, have enabled species abundance estimation of toothed whales. But standardized, user-friendly and open access automated detection and classification workflows are in demand for this key conservation metric to be realized for bats. We used the PAMGuard toolbox including its new deep learning classification module to test the performance of four open-source workflows for automated analyses of acoustic datasets from bats. Each workflow used a different initial detection algorithm followed by the same deep learning classification algorithm and was evaluated against the performance of an expert manual analyst. Workflow performance depended strongly on the signal-to-noise ratio and detection algorithm used: the full deep learning workflow had the best classification accuracy (≤67%) but was computationally too slow for practical large-scale bat PAM. Workflows using PAMGuard's detection module or triggers onboard an SM4BAT or AudioMoth accurately classified up to 47%, 59% and 34%, respectively, of calls to species. Not all workflows included noise sampling critical to estimating changes in detection probability over time, a vital parameter for abundance estimation. The workflow using PAMGuard's detection module was 40 times faster than the full deep learning workflow and missed as few calls (recall for both 0.6), thus balancing computational speed and performance. We show that complete acoustic detection and classification workflows for bat PAM data can be efficiently automated using open-source software such as PAMGuard and exemplify how detection choices, whether pre- or post-deployment, hardware or software-driven, affect the performance of deep learning classification and the downstream ecological information that can be extracted from acoustic recordings. In particular, understanding and quantifying detection/classification accuracy and the probability of detection are key to avoid introducing biases that may ultimately affect the quality of data for ecological management.
CRIS-Team Scopus Importer:2023-06-02
Keywords: automated; bat; classification; deep learning; detection; echolocation; open source; passive acoustic monitoring

Optical Coherence Tomography Angiography Characteristics of Iris Melanocytic Tumors - Skalet, Alison; Li, Yan; Lu, Chen D.; Jia, Yali; Lee, Byungkun; Husvogt, Lennart; Maier, Andreas; Fujimoto, James G.; Thomas, Charles R.; Huang, David (Ophthalmology, 2017)
Purpose To evaluate tumor vasculature with optical coherence tomography angiography (OCTA) in malignant iris melanomas and benign iris lesions. Design Cross-sectional observational clinical study. Participants Patients with iris lesions and healthy volunteers. Methods Eyes were imaged using OCTA systems operating at 1050- and 840-nm wavelengths. Three-dimensional OCTA scans were acquired. Iris melanoma patients treated with radiation therapy were imaged again after I-125 plaque brachytherapy at 6 and 18 months. Main Outcome Measures OCT and OCTA images, qualitative evaluation of iris and tumor vasculature, and quantitative vessel density. Results One eye each of 8 normal volunteers and 9 patients with iris melanomas or benign iris lesions, including freckles, nevi, and an iris pigment epithelial (IPE) cyst, were imaged. The normal iris has radially oriented vessels within the stroma on OCTA. Penetration of flow signal in normal iris depended on iris color, with best penetration seen in light to moderately pigmented irides. Iris melanomas demonstrated tortuous and disorganized intratumoral vasculature. In 2 eyes with nevi there was no increased vascularity; in another, fine vascular loops were noted near an area of ectropion uveae. Iris freckles and the IPE cyst did not have intrinsic vascularity. The vessel density was significantly higher within iris melanomas (34.5%±9.8%, P < 0.05) than in benign iris nevi (8.0%±1.4%) or normal irides (8.0%±1.2%). Tumor regression after radiation therapy for melanomas was associated with decreased vessel density. OCTA at 1050 nm provided better visualization of tumor vasculature and penetration through thicker tumors than at 840 nm. But in very thick tumors and highly pigmented lesions even 1050-nm OCTA could not visualize their full thickness. Interpretable OCTA images were obtained in 82% of participants in whom imaging was attempted. Conclusions This is the first demonstration of OCTA in iris tumors. OCTA may provide a dye-free, no-injection, cost-effective method for monitoring a variety of tumors, including iris melanocytic lesions, for growth and vascularity. This could be helpful in evaluating tumors for malignant transformation and response to treatment. Penetration of the OCT beam remains a limitation for highly pigmented tumors, as does the inability to image the entire iris in a single field.
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.optica

Optical Coherence Tomography - Husvogt, Lennart; Ploner, Stefan; Maier, Andreas (Medical Imaging Systems, 2018)
UnivIS-Import:2019-03-21:Pub.2018.tech.IMMD.IMMD5.optica
Keywords: optical; coherence; tomography

Optimal Combination of Mother Wavelet and AI Model for Precise Classification of Pediatric Electroretinogram Signals - Kulyabin, Mikhail; Zhdanov, Aleksei; Dolganov, Anton; Maier, Andreas (Sensors, 2023)

Optimization-based Extrapolation for Truncation Correction - Maier, Andreas; Scholz, Bernhard; Dennerlein, Frank (Proceedings of the second international conference on image formation in x-ray computed tomography, 2012)

Optimizing CT Scan Geometries With and Without Gradients - Thies, Mareike; Wagner, Fabian; Maul, Noah; Pfaff, Laura; Schneider, Linda-Sophie; Syben, Christopher; Maier, Andreas (17th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine (Fully3D), 2023)
In computed tomography (CT), the projection geometry used for data acquisition needs to be known precisely to obtain a clear reconstructed image. Rigid patient motion is a cause for misalignment between measured data and employed geometry. Commonly, such motion is compensated by solving an optimization problem that, e.g., maximizes the quality of the reconstructed image with respect to the projection geometry. So far, gradient-free optimization algorithms have been utilized to find the solution for this problem. Here, we show that gradient-based optimization algorithms are a possible alternative and compare the performance to their gradient-free counterparts on a benchmark motion compensation problem. Gradient-based algorithms converge substantially faster while being comparable to gradient-free algorithms in terms of capture range and robustness to the number of free parameters. Hence, gradient-based optimization is a viable alternative for the given type of problems.<br

Optimizing R with SparkR on a commodity cluster for biomedical research - Sedlmayr, Martin; Würfl, Tobias; Maier, Christian; Haeberle, Lothar; Fasching, Peter; Prokosch, Hans-Ulrich; Christoph, Jan (Computer Methods and Programs in Biomedicine, 2016)
Medical researchers are challenged today by the enormous amount of data collected in healthcare. Analysis methods such as genome-wide association studies (GWAS) are often computationally intensive and thus require enormous resources to be performed in a reasonable amount of time. While dedicated clusters and public clouds may deliver the desired performance, their use requires upfront financial efforts or anonymous data, which is often not possible for preliminary or occasional tasks. We explored the possibilities to build a private, flexible cluster for processing scripts in R based on commodity, non-dedicated hardware of our department.For this, a GWAS-calculation in R on a single desktop computer, a Message Passing Interface (MPI)-cluster, and a SparkR-cluster were compared with regards to the performance, scalability, quality, and simplicity.The original script had a projected runtime of three years on a single desktop computer. Optimizing the script in R already yielded a significant reduction in computing time (2 weeks). By using R-MPI and SparkR, we were able to parallelize the computation and reduce the time to less than three hours (2.6 h) on already available, standard office computers. While MPI is a proven approach in high-performance clusters, it requires rather static, dedicated nodes. SparkR and its Hadoop siblings allow for a dynamic, elastic environment with automated failure handling. SparkR also scales better with the number of nodes in the cluster than MPI due to optimized data communication.R is a popular environment for clinical data analysis. The new SparkR solution offers elastic resources and allows supporting big data analysis using R even on non-dedicated resources with minimal change to the original code. To unleash the full potential, additional efforts should be invested to customize and improve the algorithms, especially with regards to data distribution.
EVALuna2:174

ORCA-CLEAN: A Deep Denoising Toolkit for Killer Whale Communication - Bergler, Christian; Schmitt, Manuel; Maier, Andreas; Smeele, Simeon; Barth, Volker; Nöth, Elmar (Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH 2020, 2020)
Keywords: Killer Whale; Denoising; Call Type; Deep Learning; Orca

ORCA-PARTY: An Automatic Killer Whale Sound Type Separation Toolkit Using Deep Learning - Bergler, Christian; Schmitt, Manuel; Maier, Andreas; Cheng, Rachael Xi; Barth, Volker; Nöth, Elmar (ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022)
Data-driven and machine-based analysis of massive bioacoustic data collections, in particular acoustic regions containing a substantial number of vocalizations events, is essential and extremely valuable to identify recurring vocal paradigms. However, these acoustic sections are usually characterized by a strong incidence of overlapping vocalization events, a major problem severely affecting subsequent human-/machine- based analysis and interpretation. Robust machine-driven signal separation of species-specific call types is extremely challenging due to missing ground truth data, speaker/source- relevant information, limited knowledge about inter- and intra-call type variations, next to diverse recording conditions. The current study is the first introducing a fully- automated deep signal separation approach for overlapping orca vocalizations, addressing all of the previously mentioned challenges, together with one of the largest bioacoustic data archives recorded on killer whales (Orcinus Orca). Incorporating ORCA-PARTY as additional data enhancement step for downstream call type classification demonstrated to be extremely valuable. Besides the proof of cross-domain applicability and consistently promising results on non-overlapping signals, significant improvements were achieved when processing acoustic orca segments comprising a multitude of vocal activities. Apart from auspicious visual inspections, a final numerical evaluation on an unseen dataset proved that about 30 % more known sound patterns could be identified.<br
Keywords: Killer Whale; Deep Learning; Call Type

ORCA-SLANG: An Automatic Multi-Stage Semi-Supervised Deep Learning Framework for Large-Scale Killer Whale Call Type Identification - Bergler, Christian; Schmitt, Manuel; Maier, Andreas; Symonds, Helena; Spong, Paul; Ness, Steven; Tzanetakis, George; Nöth, Elmar (Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH 2021, 2021)
Keywords: Killer Whale; Deep Learning; Call Type

ORCA-SPOT: An Automatic Killer Whale Sound Detection Toolkit Using Deep Learning - Bergler, Christian; Schröter, Hendrik; Cheng, Rachael Xi; Barth, Volker; Weber, Michael; Nöth, Elmar; Hofer, Heribert; Maier, Andreas (Scientific Reports, 2019)
Large bioacoustic archives of wild animals are an important source to identify reappearing communication patterns, which can then be related to recurring behavioral patterns to advance the current understanding of intra-specific communication of non-human animals. A main challenge remains that most large-scale bioacoustic archives contain only a small percentage of animal vocalizations and a large amount of environmental noise, which makes it extremely difficult to manually retrieve sufficient vocalizations for further analysis – particularly important for species with advanced social systems and complex vocalizations. In this study deep neural networks were trained on 11,509 killer whale (Orcinus orca) signals and 34,848 noise segments. The resulting toolkit ORCA-SPOT was tested on a large-scale bioacoustic repository – the Orchive – comprising roughly 19,000 hours of killer whale underwater recordings. An automated segmentation of the entire Orchive recordings (about 2.2 years) took approximately 8 days. It achieved a time-based precision or positive-predictive-value (PPV) of 93.2% and an area-under-the-curve (AUC) of 0.9523. This approach enables an automated annotation procedure of large bioacoustics databases to extract killer whale sounds, which are essential for subsequent identification of significant communication patterns. The code will be publicly available in October 2019 to support the application of deep learning to bioaoucstic research. ORCA-SPOT can be adapted to other animal species.
CRIS-Team Scopus Importer:2019-08-06

ORCA-SPY enables killer whale sound source simulation, detection, classification and localization using an integrated deep learning-based segmentation - Hauer, Christopher; Nöth, Elmar; Barnhill, Alexander; Maier, Andreas; Guthunz, Julius; Hofer, Heribert; Cheng, Rachael Xi; Barth, Volker; Bergler, Christian (Scientific Reports, 2023)
Acoustic identification of vocalizing individuals opens up new and deeper insights into animal communications, such as individual-/group-specific dialects, turn-taking events, and dialogs. However, establishing an association between an individual animal and its emitted signal is usually non-trivial, especially for animals underwater. Consequently, a collection of marine species-, array-, and position-specific ground truth localization data is extremely challenging, which strongly limits possibilities to evaluate localization methods beforehand or at all. This study presents ORCA-SPY, a fully-automated sound source simulation, classification and localization framework for passive killer whale (Orcinus orca) acoustic monitoring that is embedded into PAMGuard, a widely used bioacoustic software toolkit. ORCA-SPY enables array- and position-specific multichannel audio stream generation to simulate real-world ground truth killer whale localization data and provides a hybrid sound source identification approach integrating ANIMAL-SPOT, a state-of-the-art deep learning-based orca detection network, followed by downstream Time-Difference-Of-Arrival localization. ORCA-SPY was evaluated on simulated multichannel underwater audio streams including various killer whale vocalization events within a large-scale experimental setup benefiting from previous real-world fieldwork experience. Across all 58,320 embedded vocalizing killer whale events, subject to various hydrophone array geometries, call types, distances, and noise conditions responsible for a signal-to-noise ratio varying from - 14.2 dB to 3 dB, a detection rate of 94.0 % was achieved with an average localization error of 7.01 ∘ . ORCA-SPY was field-tested on Lake Stechlin in Brandenburg Germany under laboratory conditions with a focus on localization. During the field test, 3889 localization events were observed with an average error of 29.19 ∘ and a median error of 17.54 ∘ . ORCA-SPY was deployed successfully during the DeepAL fieldwork 2022 expedition (DLFW22) in Northern British Columbia, with a mean average error of 20.01 ∘ and a median error of 11.01 ∘ across 503 localization events. ORCA-SPY is an open-source and publicly available software framework, which can be adapted to various recording conditions as well as animal species.
CRIS-Team Scopus Importer:2023-07-21

ORCA-SPY: Killer Whale Sound Source Simulation and Detection, Classification and Localization in PAMGuard Utilizing Integrated Deep Learning Based Segmentation - Hauer, Christopher; Nöth, Elmar; Barnhill, Alexander; Maier, Andreas; Guthunz, Julius; Hofer, Heribert; Cheng, Rachael Xi; Barth, Volker; Bergler, Christian (Scientific Reports, 2023)
Acoustic identification of vocalizing individuals opens up new and deeper insights into animal communications, such as individual-/group-specific dialects, turn-taking events, and dialogs. However, establishing an association between an individual animal and its emitted signal is usually non-trivial, especially for animals underwater. Consequently, a collection of marine species-, array-, and position-specific ground truth localization data is extremely challenging, which strongly limits possibilities to evaluate localization methods beforehand or at all. This study presents ORCA-SPY, a fully-automated sound source simulation, classification and localization framework for passive killer whale (Orcinus orca) acoustic monitoring that is embedded into PAMGuard, a widely used bioacoustic software toolkit. ORCA-SPY enables array- and position-specific multichannel audio stream generation to simulate real-world ground truth killer whale localization data and provides a hybrid sound source identification approach integrating ANIMAL-SPOT, a state-of-the-art deep learning-based orca detection network, followed by downstream Time-Difference-Of-Arrival (TDOA) localization. ORCA-SPY was evaluated on simulated multichannel underwater audio streams including various killer whale vocalization events within a large-scale experimental setup benefiting from previous real-world fieldwork experience. Across all 58,320 embedded vocalizing killer whale events, subject to various hydrophone array geometries, call types, distances, and noise conditions responsible for a signal-to-noise ratio varying from -14.2dB to 3dB, a detection rate of 94.0 % was achieved with an average localization error of 7.01°. ORCA-SPY was field-tested on the lake Stechlin under laboratory conditions with a focus on localization. During the field test, 3889 localization events were observed with an average error of 29.19° and a median error of 17.54°. ORCA-SPY was deployed successfully at the DeepAL fieldwork 2022 expedition (DLFW22) in Northern British Columbia, with a mean average error of 20.01° and a median error of 11.01° across 503 localization events. ORCA-SPY is an open-source and publicly available software framework, which can be adapted to various recording conditions as well as animal species.

ORCA-WHISPER: An Automatic Killer Whale Sound Type Generation Toolkit Using Deep Learning - Bergler, Christian; Barnhill, Alexander; Perrin, Dominik; Schmitt, Manuel; Maier, Andreas; Nöth, Elmar (Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH 2022, 2022)
Keywords: Killer Whale; Deep Learning; Call Type; Generative Adversarial Networks

Organ-specific context-sensitive CT image reconstruction and display - Dorn, Sabrina; Chen, Shuqing; Sawall, Stefan; Simons, David; May, Matthias; Maier, Joscha; Knaup, Michael; Schlemmer, Heinz-Peter; Maier, Andreas; Lell, Michael; Kachelriess, Marc (Proc.SPIE, 2018)
In this work, we present a novel method to combine mutually exclusive CT image properties that emerge from different reconstruction kernels and display settings into a single organ-specific image reconstruction and display. We propose a context-sensitive reconstruction that locally emphasizes desired image properties by exploiting prior anatomical knowledge. Furthermore, we introduce an organ-specific windowing and display method that aims at providing a superior image visualization. Using a coarse-to-fine hierarchical 3D fully convolutional network (3D U-Net), the CT data set is segmented and classified into different organs, e.g. the heart, vasculature, liver, kidney, spleen and lung, as well as into the tissue types bone, fat, soft tissue and vessels. Reconstruction and display parameters most suitable for the organ, tissue type, and clinical indication are chosen automatically from a predefined set of reconstruction parameters on a per-voxel basis. The approach is evaluated using patient data acquired with a dual source CT system. The final context-sensitive images simultaneously link the indication-specific advantages of different parameter settings and result in images joining tissue-related desired image properties. A comparison with conventionally reconstructed and displayed images reveals an improved spatial resolution in highly attenuating objects and air while maintaining a low noise level in soft tissue in the compound image. The images present significantly more information to the reader simultaneously and dealing with multiple volumes may no longer be necessary. The presented method is useful for the clinical workflow and bears the potential to increase the rate of incidental findings.
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.organs

Organ-specific context-sensitive single and dual energy CT (DECT) image reconstruction, display and analysis - Dorn, Sabrina; Chen, Shuqing; Pisana, Francesco; Maier, Joscha; Knaup, Michael; Sawall, Stefan; Maier, Andreas; Lell, Michael; Kachelriess, Marc (n 103rd Scientific Assembly and Annual Meeting of the Radiological Society of North America (RSNA), 2017)
UnivIS-Import:2018-09-06:Pub.2017.tech.IMMD.IMMD5.organs

Organ-Specific vs. Patient Risk-Specific Tube Current Modulation in Thorax CT Scans Covering the Female Breast - Klein, Laura; Enzmann, Lucia; Byl, Achim; Liu, Chang; Sawall, Stefan; Maier, Andreas; Maier, Joscha; Lell, Michael; Kachelrieß, Marc (Proceedings of SPIE - The International Society for Optical Engineering, 2022)
An important goal in modern CT imaging is reducing the dose delivered to patients especially to risk-relevant organs. This work compares the clinically applied dose reduction techniques mAs-minimizing tube current modulation (mAsTCM) and a typical organ-specific TCM (osTCM, here: X-Care, Siemens Healthcare) with a novel radiation risk-minimizing tube current modulation (riskTCM) with a focus on the dose delivered to the female breast. The mAsTCM minimizes the mAs product as a surrogate parameter for the patient dose but does not consider the different organs' risks. In contrast, osTCM aims to minimize the dose delivered to the female breast by reducing the tube current for anterior projections. The riskTCM minimizes the patient risk by minimizing the effective dose to the patient, which is done by taking accounting for the organ doses. In this study, the dose reduction effect of the TCM techniques is compared by simulations based on clinical CT scans. Thereby, riskTCM reduces the effective dose by up to 35% in comparison to mAsTCM, and by up to 30% in comparison to osTCM depending on the anatomical region and the patient.
CRIS-Team Scopus Importer:2022-11-25
Keywords: chest imaging; CT; dose reduction; tube current modulation

Out-of-the-box Calving Front Detection Method Using Deep Learning - Herrmann, Oskar; Gourmelon, Nora; Seehaus, Thorsten; Maier, Andreas; Fürst, Johannes; Braun, Matthias; Christlein, Vincent (Cryosphere, 2023)

Overexposure Correction by Mixed One-bit Compressive Sensing for C-Arm CT - Huang, Xiaolin; Xia, Yan; Huang, Yixing; Hornegger, Joachim; Maier, Andreas (Bildverarbeitung für die Medizin: Algorithmen-Systeme-Anwendungen, 2017)
This paper proposes a novel method to deal with overexposure for C-arm CT reconstruction. The proposed method is based on recent progress of one bit compressive sensing (1bit-CS), which is to recover sparse signals from sign measurements. Overexposure could be regarded as a kind of sign information, thus the application of 1bit-CS to overexposure correction in CT reconstruction is expected. This method is evaluated on a phantom and its promising performance implies potential application on clinical data.
UnivIS-Import:2018-09-06:Pub.2017.tech.IMMD.IMMD5.overex
Keywords: Overexposure; Mixed One-bit; Compressive Sensing

Over-Exposure Correction in CT Using Optimization-Based Multiple Cylinder Fitting - Preuhs, Alexander; Berger, Martin; Xia, Yan; Maier, Andreas; Hornegger, Joachim; Fahrig, Rebecca (Bildverarbeitung für die Medizin 2015, 2015)
UnivIS-Import:2015-04-17:Pub.2015.tech.IMMD.IMMD5.overex_3
Keywords: GRK-1773

Over-exposure correction in knee cone-beam CT imaging with automatic exposure control using a partial low dose scan - Choi, Jang-Hwan; Müller, Kerstin; Hsieh, Scott; Maier, Andreas; Gold, Garry; Levenstone, Marc; Fahrig, Rebecca (Medical Imaging 2016: Physics of Medical Imaging, 2016)
UnivIS-Import:2016-06-01:Pub.2016.tech.IMMD.IMMD5.overex
Keywords: weight-bearing; reconstruction; computed tomography

Pan-tumor CAnine cuTaneous Cancer Histology (CATCH) dataset - Wilm, Frauke; Fragoso, Marco; Marzahl, Christian; Qiu, Jingna; Puget, Chloe; Diehl, Laura; Bertram, Christof A.; Klopfleisch, Robert; Maier, Andreas; Breininger, Katharina; Aubreville, Marc (Scientific Data, 2022)
Due to morphological similarities, the differentiation of histologic sections of cutaneous tumors into individual subtypes can be challenging. Recently, deep learning-based approaches have proven their potential for supporting pathologists in this regard. However, many of these supervised algorithms require a large amount of annotated data for robust development. We present a publicly available dataset of 350 whole slide images of seven different canine cutaneous tumors complemented by 12,424 polygon annotations for 13 histologic classes, including seven cutaneous tumor subtypes. In inter-rater experiments, we show a high consistency of the provided labels, especially for tumor annotations. We further validate the dataset by training a deep neural network for the task of tissue segmentation and tumor subtype classification. We achieve a class-averaged Jaccard coefficient of 0.7047, and 0.9044 for tumor in particular. For classification, we achieve a slide-level accuracy of 0.9857. Since canine cutaneous tumors possess various histologic homologies to human tumors the added value of this dataset is not limited to veterinary pathology but extends to more general fields of application.
CRIS-Team Scopus Importer:2022-10-14

Pan-tumor T-lymphocyte detection using deep neural networks: Recommendations for transfer learning in immunohistochemistry - Wilm, Frauke; Ihling, Christian; Méhes, Gábor; Terracciano, Luigi; Puget, Chloé; Klopfleisch, Robert; Schüffler, Peter; Aubreville, Marc; Maier, Andreas; Mrowiec, Thomas; Breininger, Katharina (Journal of Pathology Informatics, 2023)
The success of immuno-oncology treatments promises long-term cancer remission for an increasing number of patients. The response to checkpoint inhibitor drugs has shown a correlation with the presence of immune cells in the tumor and tumor microenvironment. An in-depth understanding of the spatial localization of immune cells is therefore critical for understanding the tumor's immune landscape and predicting drug response. Computer-aided systems are well suited for efficiently quantifying immune cells in their spatial context. Conventional image analysis approaches are often based on color features and therefore require a high level of manual interaction. More robust image analysis methods based on deep learning are expected to decrease this reliance on human interaction and improve the reproducibility of immune cell scoring. However, these methods require sufficient training data and previous work has reported low robustness of these algorithms when they are tested on out-of-distribution data from different pathology labs or samples from different organs. In this work, we used a new image analysis pipeline to explicitly evaluate the robustness of marker-labeled lymphocyte quantification algorithms depending on the number of training samples before and after being transferred to a new tumor indication. For these experiments, we adapted the RetinaNet architecture for the task of T-lymphocyte detection and employed transfer learning to bridge the domain gap between tumor indications and reduce the annotation costs for unseen domains. On our test set, we achieved human-level performance for almost all tumor indications with an average precision of 0.74 in-domain and 0.72–0.74 cross-domain. From our results, we derive recommendations for model development regarding annotation extent, training sample selection, and label extraction for the development of robust algorithms for immune cell scoring. By extending the task of marker-labeled lymphocyte quantification to a multi-class detection task, the pre-requisite for subsequent analyses, e.g., distinguishing lymphocytes in the tumor stroma from tumor-infiltrating lymphocytes, is met.
CRIS-Team Scopus Importer:2023-07-21
Keywords: Deep learning; Domain adaptation; Immuno-oncology; Immunohistochemistry; Transfer learning; Tumor-infiltrating lymphocytes

Papoulis-Gerchberg Algorithms for Limited Angle Tomography Using Data Consistency Conditions - Huang, Yixing; Taubmann, Oliver; Huang, Xiaolin; Lauritsch, Günter; Maier, Andreas (Proceedings of the 5th International Conference on Image Formation in X-ray Computed Tomography, 2018)
The Papoulis-Gerchberg (P-G) algorithm is widely used for extrapolation of band-limited signals. It is applicable to limited angle tomography as well since typical imaged objects in computed tomography have a limited spatial extent, which means that the Fourier transforms of the objects can be considered band-limited signals. In computed tomography, some other bandlimitation properties have been discovered as well, which are referred to as data consistency conditions. For example, the Fourier transform of a parallel-beam sinogram has an empty double-wedge region. The Chebyshev-Fourier transform of a parallel-beam sinogram only has nonzero values inside a wedge region and these values form a checkerboard pattern, which is Helgason-Ludwig consistency condition. In this paper, we propose two P-G algorithms to restore missing data in limited angle tomography using the above two consistency conditions. Numerical experiments on the Shepp-Logan phantom demonstrate that they can reduce streaks better than the conventional P-G algorithm. <br
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.papoul_8
Keywords: limited angle tomography; Papoulis-Gerchberg; artifact reduction; data consistency

Parallel Robust Speech Recognition - Maier, Andreas (VDM Verlag Dr. Müller, 2008)

Parallel-Shift Tomosynthesis for Orthopedic Applications - Luckner, Christoph; Sesselmann, Stefan; Mertelmeier, Thomas; Maier, Andreas; Ritschl, Ludwig (Proc. SPIE, 2018)
The upsurge in interest of digital tomosynthesis is mainly caused by breast imaging; however, it finds more and more attention in orthopedic imaging as well. Offering a superior in-plane resolution compared to CT imaging and the additional depth information compared to conventional 2-D X-ray images, tomosynthesis may be an interesting complement to the other two imaging modalities. Additionally, a tomosynthesis scan is likely to be faster and the radiation dose is considerably below that of a CT. Usually, a tomosynthetic acquisition focuses only on one body part as the common acquisition techniques restrict the field-of-view. We propose a method which is able to perform full-body acquisitions with a standard X-ray system by shifting source and detector simultaneously in parallel planes without the need to calibrate the system beforehand. Furthermore, a novel aliasing filter is introduced which addresses the impact of the non-isotropic resolution during the reconstruction. We provide images obtained by filtered as well as unfiltered backprojection and discuss the influence of the scanning angle as well as the reconstruction filter on the reconstructed images. We found from the experiments that our method shows promising results especially for the imaging of anatomical structures which are usually obscured by each other since the depth resolution allows to distinguish between these structures. Additionally, as of the high isotropic in-plane spatial resolution of the tomographic volume, it is easily possible to perform precise measurements which are a crucial task, e. g. during the planning of orthopedic surgeries or the assessment of pathologies like scoliosis or subtle fractures.
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.parall
Keywords: Digital Tomosynthesis; Tomography; Aliasing Filter; Reconstruction; Full-body imaging; Orthopedics

Parallel Variable Interscan Time Analysis (VISTA) OCTA and en Face Doppler OCT of Optic Disc and Peripapillary Vasculature - Lee, Byungkun; Moult, Eric M.; Ploner, Stefan; Alibhai, A. Yasin; Rebhun, Carl B.; Moreira-Neto, Carlos; Husvogt, Lennart; Maier, Andreas; Wollstein, Gadi; Schuman, Joel; Waheed, Nadia K.; Duker, Jay S.; et al. (Investigative Ophthalmology & Visual Science, 2017)
Purpose : The introduction of optical coherence tomography angiography (OCTA) allows clinicians to study the microvascular alterations that occur in diabetic retinopathy (DR). Standard OCTA systems yield a detailed picture of the presence of vasculature, but have limited dynamic range and provide little information about blood flow speeds. Our group has recently developed variable interscan time analysis (VISTA), which provides a framework for studying relative blood flow speeds using OCTA. This study investigates VISTA to assess microvascular blood flow speeds in eyes with DR. Methods : Using a VISTA protocol we analyzed 3 eyes with mild non-proliferative DR (NPDR) (2 patients; 66.7±2.31 y/o), 5 eyes with moderate NPDR (3 patients; 57.6±16.58 y/o), 5 eyes with severe NPDR (4 patients; 51.8±13.44 y/o), and 8 eyes with proliferative DR (PDR) (6 patients; 57.0±13.38 y/o). Results : We observed a range of flow distributions associated with capillary looping/microaneurysms, tortuous vessels, dilated vessels, and neovasculature vessels. Representative VISTA-OCTA images are shown in Figures 1 and 2. Conclusions : VISTA is a useful framework for studying blood flow speeds in eyes with D
UnivIS-Import:2018-09-06:Pub.2017.tech.IMMD.IMMD5.parall
Keywords: oct; octa; oct angiography; vista; variable interscan time analysis; blood flow; doppler oct

Parameterization of voice onset for automatic assessment of Parkinson’s disease - Arias Vergara, Tomás; Schraut, Tobias; Orozco-Arroyave, Juan R.; Döllinger, Michael (Journal of the Acoustical Society of America, 2022)
Acoustic analysis of Parkinson’s disease (PD) usually focuses on sustained oscillations of the vocal folds; however, the voice onset (transition from the rest state to a saturation value) is often neglected. In this study, we investigated the parameterization of the voice onset for the objective evaluation of PD. 50 PD patients (25 females) and 50 healthy controls (25 females) performed the sustained phonation of the vowels /ah/, /ih/, and /uh/. Three experts listened to the recordings and assessed the dysarthria level according to a modified version of the Frenchay dysarthria assessment scale (mFDA). We extracted the voice onset automatically, considering the time needed for the amplitude envelope of the acoustic signal to go from 10% up to 90% of the maximum value. Then, we computed filter bank features from the voice onset and pitch, loudness, and perturbation features from the sustained phonation. We performed automatic classification and regression analyses using support vector machines. We obtained a classification accuracy of 89% (AUC: 0.93) when we used feature importance analysis to reduce the feature set. In addition, the regression analysis showed a significant correlation (r: 0.583, p-value < 0.001) between the acoustic features and the mFDA scale. These results show the suitability of the voice onset for objective clinical evaluation of PD.<br
Keywords: Phonetics; Vocal folds; Acoustic modeling; simulation and analysis; Speech perception; Acoustic signal processing; Vowel systems; Machine learning; Regression analysis

Parameterization of voice onset for automatic assessment of Parkinson’s disease - Arias Vergara, Tomás; Schraut, Tobias; Orozco-Arroyave, Juan R.; Döllinger, Michael (Journal of the Acoustical Society of America, 2022)
Acoustic analysis of Parkinson’s disease (PD) usually focuses on sustained oscillations of the vocal folds; however, the voice onset (transition from the rest state to a saturation value) is often neglected. In this study, we investigated the parameterization of the voice onset for the objective evaluation of PD. 50 PD patients (25 females) and 50 healthy controls (25 females) performed the sustained phonation of the vowels /ah/, /ih/, and /uh/. Three experts listened to the recordings and assessed the dysarthria level according to a modified version of the Frenchay dysarthria assessment scale (mFDA). We extracted the voice onset automatically, considering the time needed for the amplitude envelope of the acoustic signal to go from 10% up to 90% of the maximum value. Then, we computed filter bank features from the voice onset and pitch, loudness, and perturbation features from the sustained phonation. We performed automatic classification and regression analyses using support vector machines. We obtained a classification accuracy of 89% (AUC: 0.93) when we used feature importance analysis to reduce the feature set. In addition, the regression analysis showed a significant correlation (r: 0.583, p-value < 0.001) between the acoustic features and the mFDA scale. These results show the suitability of the voice onset for objective clinical evaluation of PD.<br

Parametric LV Model Fitting to Coronary Arteries - Geimer, Tobias; Höhn, Johannes; Unberath, Mathias; Maier, Andreas (2017 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC), 2017)
X-ray angiography is the gold standard in assessing coronary artery diseases. With research focus shifting towards 3D+t applications, heart models that allow for the extraction of functional parameters from the heart motion receive increasing attention. We present an approach to fit a parametric left ventricular heart model originally developed for tagged MRI to the centerlines of coronary arteries reconstructed from rotational coronary angiography. This 3D model fitting process must accommodate the sparse point set conditional to the underlying angiography data. Using a coarse-to-fine optimization based on simulated annealing and ellipsoid pseudo-distances, we achieve a reprojection error of 0.794 mm for the surface points compared to 0.422 mm of the 3D centerline ground truth. Results are promising and form the basis to extend the model to 3D+t in order to monitor radial and longitudinal contraction as well as left ventricular twist over the cardiac cycle.<br
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.parame_9

Partial Fourier Reconstruction in Liver DWI using a Recurrent Convolutional Network - Gadjimuradov, Fasil; Benkert, Thomas; Nickel, Marcel Dominik; Maier, Andreas (ISMRM & SMRT Annual Conference & Exhibition, 2021)

Particle filter de-noising of voxel-specific time-activity-curves in personalized 177Lu therapy - Götz, Theresa; Götz, Th I.; Lang, E. W.; Schmidkonz, Christian; Maier, Andreas; Kuwert, Torsten; Ritt, Philipp (Zeitschrift für Medizinische Physik, 2019)
Background: Currently, there is a high interest in 177Lu targeted radionuclide therapies, which could be attributed to favorable results obtained from 177Lu compounds targeting neuro-endocrine and prostate tumors. SPECT based dosimetry could be used for deriving dose values for individual voxels, as is the standard in external-beam radiation-therapy (EBRT). For this a time-activity-curve (TAC) at voxel resolution and also a voxel-wise modeling of radiation energy deposition are necessary. But a voxel-wise determination of TACs is problematic, since several confounding factors exist, such as e.g. poor count-statistics or registration inaccuracies, which add noise to the observed activity states. A particle filter (PF) is a class of methods which applies regularization based on a model of the temporal evolution of activity states. The aim of this study is to introduce the application of PFs for de-noising of per-voxel time-activity curves. Methods: We applied a PF for de-noising the TACs of 26 patients, who underwent 177Lu-DOTATOC or -PSMA therapy. The TACs were obtained from fully-quantitative, serial SPECT(/CT) data, acquired at 4 h, 24 h, 48 h, 72 h p.i. The model used in the PF was a mono-exponential decay and its free parameters were determined based on objective criteria. The time-integrated activities (TIA) resulting from the PF (PFF) were compared to the results of a mono-exponential fit (SF) of individual voxels in several volumes of interest (kidneys, spleen, tumors). Additionally, an organ-averaged TIA was derived from whole-organ VOIs and subsequent curve-fitting. This whole-organ TIA was also compared to the whole-organ TIAs obtained from summation of the voxel-wise TIAs from PFF and SF. Results: The number of particles was set to 1000. Optimal values for noise of observations and noise of the model were 0.25 and 0.5, respectively. The deviation of whole-organ TIAs from conventional organ-based dosimetry and the summation of the voxel-wise TIAs was substantial for SF (kidneys −22.3%, spleen −49.6%, tumor −60.0%), as well as for PFF (kidneys −37.1%, spleen −57.9%, tumor −70.9%). The distribution of voxel-wise half-lives resulting from the PFF method was considerably closer to the organ-averaged value, and the number of implausibly long half-lives (>physical HL) was reduced. Conclusion: The PFF leads to voxel-wise half-lives, which are more plausible than those resulting from SF. However, one has to admit that voxel-wise fitting generally leads to considerable deviations from the organ-averaged TIA as obtained by conventional whole-organ evaluation. Unfortunately, we did not have ground-truth TIA of our patient data and proper ground-truth could even be impossible to obtain. Nevertheless, there are strong indicators that particle filtering can be used for reducing voxel-wise TAC noise.
CRIS-Team Scopus Importer:2019-12-27
Keywords: Dosimetry; Nuclear medicine; Time-activity-curve; Voxelwise

Patch-based Carcinoma Detection on Confocal Laser Endomicroscopy Images - A Cross-Site Robustness Assessment - Aubreville, Marc; Goncalves, Miguel; Knipfer, Christian; Oetter, Nicolai; Würfl, Tobias; Neumann, Helmut; Stelzle, Florian; Bohr, Christopher; Maier, Andreas (Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018), 2018)
Deep learning technologies such as convolutional neural networks (CNN) provide powerful methods for image recognition and have recently been employed in the field of automated carcinoma detection in confocal laser endomicroscopy (CLE) images. CLE is a (sub-)surface microscopic imaging technique that reaches magnifications of up to 1000x and is thus suitable for in vivo structural tissue analysis. In this work, we aim to evaluate the prospects of a priorly developed deep learning-based algorithm targeted at the identification of oral squamous cell carcinoma with regard to its generalization to further anatomic locations of squamous cell carcinomas in the area of head and neck. We applied the algorithm on images acquired from the vocal fold area of five patients with histologically verified squamous cell carcinoma and presumably healthy control images of the clinically normal contra-lateral vocal cord. We find that the network trained on the oral cavity data reaches an accura

Patient-bounded Extrapolation for 3D Region of Interest Reconstruction in C-arm CT - Xia, Yan; Bauer, Sebastian; Maier, Andreas; Berger, Martin; Hornegger, Joachim (Proceedings of the third international conference on image formation in x-ray computed tomography, 2014)
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.patien_1
Keywords: GRK-1773

Patient-bounded extrapolation using low-dose priors for volume-of-interest imaging in C-arm CT - Xia, Yan; Bauer, Sebastian; Maier, Andreas; Berger, Martin; Hornegger, Joachim (Medical Physics, 2015)
Purpose: Three-dimensional (3D) volume-of-interest (VOI) imaging with C-arm systems provides anatomical information in a predefined 3D target region at a considerably low x-ray dose. However, VOI imaging involves laterally truncated projections from which conventional reconstruction algorithms generally yield images with severe truncation artifacts. Heuristic based extrapolation methods, e.g., water cylinder extrapolation, typically rely on techniques that complete the truncated data by means of a continuity assumption and thus appear to be ad-hoc. It is our goal to improve the image quality of VOI imaging by exploiting existing patient-specific prior information in the workflow. Methods: A necessary initial step prior to a 3D acquisition is to isocenter the patient with respect to the target to be scanned. To this end, low-dose fluoroscopic x-ray acquisitions are usually applied from anteriorposterior (AP) and medio-lateral (ML) views. Based on this, the patient is isocentered by repositioning the table. In this work, we present a patient-bounded extrapolation method that makes use of these noncollimated fluoroscopic images to improve image quality in 3D VOI reconstruction. The algorithm first extracts the 2D patient contours from the noncollimated AP and ML fluoroscopic images. These 2D contours are then combined to estimate a volumetric model of the patient. Forward-projecting the shape of the model at the eventually acquired C-arm rotation views gives the patient boundary information in the projection domain. In this manner, we are in the position to substantially improve image quality by enforcing the extrapolated line profiles to end at the known patient boundaries, derived from the 3D shape model estimate. Results: The proposed method was evaluated on eight clinical datasets with different degrees of truncation. The proposed algorithm achieved a relative root mean square error (rRMSE) of about 1.0% with respect to the reference reconstruction on nontruncated data, even in the presence of severe truncation, compared to a rRMSE of 8.0% when applying a state-of-the-art heuristic extrapolation technique. Conclusions: The method we proposed in this paper leads to a major improvement in image quality for 3D C-arm based VOI imaging. It involves no additional radiation when using fluoroscopic images that are acquired during the patient isocentering process. The model estimation can be readily integrated into the existing interventional workflow without additional hardware.
UnivIS-Import:2015-07-08:Pub.2015.tech.IMMD.IMMD5.patien
Keywords: computed tomography; extrapolation; fluoroscopic image; truncation correction; volume of interest imaging

Patient-individualized boundary conditions for CFD simulations using time-resolved 3D angiography - Bögel, Marco; Gehrisch, Sonja; Redel, Thomas; Rohkohl, Christopher; Dörfler, Arnd; Maier, Andreas; Kowarschik, Markus; Hölter, Philip (International Journal of Computer Assisted Radiology and Surgery, 2016)
Hemodynamic simulations are of increasing interest for the assessment of aneurysmal rupture risk and treatment planning. Achievement of accurate simulation results requires the usage of several patient-individual boundary conditions, such as a geometric model of the vasculature but also individualized inflow conditions.We propose the automatic estimation of various parameters for boundary conditions for computational fluid dynamics (CFD) based on a single 3D rotational angiography scan, also showing contrast agent inflow. First the data are reconstructed, and a patient-specific vessel model can be generated in the usual way. For this work, we optimize the inflow waveform based on two parameters, the mean velocity and pulsatility. We use statistical analysis of the measurable velocity distribution in the vessel segment to estimate the mean velocity. An iterative optimization scheme based on CFD and virtual angiography is utilized to estimate the inflow pulsatility. Furthermore, we present methods to automatically determine the heart rate and synchronize the inflow waveform to the patient's heart beat, based on time-intensity curves extracted from the rotational angiogram. This will result in a patient-individualized inflow velocity curve.The proposed methods were evaluated on two clinical datasets. Based on the vascular geometries, synthetic rotational angiography data was generated to allow a quantitative validation of our approach against ground truth data. We observed an average error of approximately [Formula: see text] for the mean velocity, [Formula: see text] for the pulsatility. The heart rate was estimated very precisely with an average error of about [Formula: see text], which corresponds to about 6 ms error for the duration of one cardiac cycle. Furthermore, a qualitative comparison of measured time-intensity curves from the real data and patient-specific simulated ones shows an excellent match.The presented methods have the potential to accurately estimate patient-specific boundary conditions from a single dedicated rotational scan.
EVALuna2:23010

Patient-specific radiation risk-based tube current modulation for diagnostic CT - Klein, Laura; Liu, Chang; Steidel, Jorg; Enzmann, Lucia; Knaup, Michael; Sawall, Stefan; Maier, Andreas; Lell, Michael; Maier, Joscha; Kachelriess, Marc (Medical Physics, 2022)
Purpose Modern CT scanners use automatic exposure control (AEC) techniques, such as tube current modulation (TCM), to reduce dose delivered to patients while maintaining image quality. In contrast to conventional approaches that minimize the tube current time product of the CT scan, referred to as mAsTCM in the following, we herein propose a new method referred to as riskTCM, which aims at reducing the radiation risk to the patient by taking into account the specific radiation risk of every dose-sensitive organ. Methods For current mAsTCM implementations, the mAs product is used as a surrogate for the patient dose. Thus, they do not take into account the varying dose sensitivity of different organs. Our riskTCM framework assumes that a coarse CT reconstruction, an organ segmentation, and an estimation of the dose distribution can be provided in real time, for example, by applying machine learning techniques. Using this information, riskTCM determines a tube current curve that minimizes a patient risk measure, for example, the effective dose, while keeping the image quality constant. We retrospectively applied riskTCM to 20 patients covering all relevant anatomical regions and tube voltages from 70 to 150 kV. The potential reduction of effective dose at same image noise is evaluated as a figure of merit and compared to mAsTCM and to a situation with a constant tube current referred to as noTCM. Results Anatomical regions like the neck, thorax, abdomen, and the pelvis benefit from the proposed riskTCM. On average, a reduction of effective dose of about 23% for the thorax, 31% for the abdomen, 24% for the pelvis, and 27% for the neck has been evaluated compared to today's state-of-the-art mAsTCM. For the head, the resulting reduction of effective dose is lower, about 13% on average compared to mAsTCM. Conclusions With a risk-minimizing TCM, significant higher reduction of effective dose compared to mAs-minimizing TCM is possible.
CRIS-Team WoS Importer:2022-05-13

Patient surface model and internal anatomical landmarks embedding - Zhong, Xia; Strobel, Norbert; Birkhold, Annette; Kowarschik, Markus; Fahrig, Rebecca; Maier, Andreas (Bildverarbeitung für die Medizin, 2018)
The patient surface model has shown to be a useful asset to improve existing diagnostic and interventional tasks in a clinical environment. For example, in combination with RGB-D cameras, a patient surface model can be used to automate and accelerate the diagnostic imaging workflow, manage patient dose, and provide navigation assistance. A shortcoming of today’s patient surface models, however, is that, internal anatomical landmarks are not present. In this paper, we introduce a method to estimate internal anatomical landmarks based on the surface model of a patient. Our method relies on two major steps. First, we fit a template surface model is to a segmented surface of a CT dataset with annotated internal landmarks using keypoint and feature descriptor based rigid alignment and atlas-based non-rigid registration. In a second step, we find for each internal landmark a neighborhood on the template surface and learn a generalized linear embedding between neighboring surface vertices in the template and the internal landmark. We trained and evaluated our method using cross-validation in 20 datasets over 50 internal landmarks. We compared the performance of four different generalized linear models. The best mean estimation error over all the landmarks was achieved using the lasso regression method with a mean error of 12.19 ± 6.98 mm.<br
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.patien_2

PEAKS-A Platform for Evaluation and Analysis of all Kinds of Speech Disorders - Maier, Andreas; Haderlein, Tino; Schuster, Maria; Nöth, Elmar (Proc. 41st Annual Meeting of the Society for Biomedical Technologies of the Association for Electrical, Electronic & Information Technologies (BMT 2007), 2007)

PEAKS - A system for the automatic evaluation of voice and speech disorders - Maier, Andreas; Haderlein, Tino; Eysholdt, Ulrich; Rosanowski, Frank; Batliner, Anton; Schuster, Maria; Nöth, Elmar (Speech Communication, 2009)

PEAKS: Ein Client-Server-Internetportal zur Bewertung der Aussprache - Maier, Andreas; Haderlein, Tino; Nöth, Elmar; Schuster, Maria (Telemed 2008 Proceedings, 2008)

PEAKS - Programm zur Evaluation und Analyse Kindlicher Sprachstörungen - Bedienungsanleitung - Maier, Andreas (2006)
UnivIS-Import:2016-06-30:Pub.2006.tech.IMMD.IMMD5.peaksp
Keywords: Peaks; Automatische Aussprache Bewertung

Pediatric Patient Surface Model Atlas Generation and X-Ray Skin Dose Estimation - Zhong, Xia; Roser, Philipp; Bayer, Siming; Ravikumar, Nishant; Strobel, Norbert; Birkhold, Annette; Horz, Tim; Kowarschik, Markus; Fahrig, Rebecca; Maier, Andreas (BVM, 2019)
Fluoroscopy is used in a wide variety of examinations and procedures to diagnose or treat patients in modern pediatric medicine. Although these image guided interventions have many advantages in treating pediatric patients, understanding the deterministic and long term stochastic effects of ionizing radiation are of particular importance for this patient demographic. Therefore, quantitative estimation and visualization of radiation exposure distribution, and dose accumulation over the course of a procedure, is crucial for intra-procedure dose tracking and long term monitoring for risk assessment. Personalized pediatric models are necessary for precise determination of patient-X-ray interactions. One way to obtain such a model is to collect data from a population of pediatric patients, establish a population based generative pediatric model and use the latter for skin dose estimation. In this paper, we generate a population model for pediatric patient using data acquired by two RGB-D cameras from different views. A generative atlas was established using template registration. We evaluated the registered templates and generative atlas by computing the mean vertex error to the associated point cloud. The evaluation results show that the mean vertex error reduced from 25.2 ± 12.9 mm using an average surface model to 18.5 ± 9.4mm using specifically estimated pediatric surface model using the generated atlas. Similarly, the dose estimation error was halved from 10.6 ± 8.5% using the average surface model to 5.9 ± 9.3% using the personalized surface estimates.<br

Penalized least-square CT reconstruction without and with statistical weights: effect on lesion detection performance with human observers - Haase, Viktor; Griffith, April; Guo, Zijia; Oktay, Mehmet; Hahn, Katharina; Schöndube, Harald; Stierstorfer, Karl; Noo, Frederic (Proceedings of Fully3D'17, 2017)
UnivIS-Import:2019-08-15:Pub.2017.tech.IMMD.IMMD5.penali_5

Phasenkontrast Röntgen mit 2 Phasengittern und medizinisch relevanten Detektoren - Bopp, Johannes; Gallersdörfer, Michael; Ludwig, Veronika; Seifert, Maria; Maier, Andreas; Anton, Gisela; Riess, Christian (Proceedings des Workshops Bildverarbeitung für die Medizin 2018 - Algorithmen - Systeme - Anwendungen, 2018)

Phase-Sensitive Region-of-Interest Computed Tomography - Felsner, Lina; Berger, Martin; Käppler, Sebastian; Bopp, Johannes; Ludwig, Veronika; Weber, Thomas; Pelzer, Georg; Michel, Thilo; Maier, Andreas; Anton, Gisela; Riess, Christian (21st International Conference on Medical Image Computing and Computer Assisted Intervention, 2018)

Phonet: A tool based on gated recurrent neural networks to extract phonological posteriors from speech - Vasquez Correa, Juan; Klumpp, Philipp; Orozco Arroyave, Juan Rafael; Nöth, Elmar (Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 2019)
There are a lot of features that can be extracted from speech signals for different applications such as automatic speech recognition or speaker verification. However, for pathological speech processing there is a need to extract features about the presence of the disease or the state of the patients that are comprehensible for clinical experts. Phonological posteriors are a group of features that can be interpretable by the clinicians and at the same time carry suitable information about the patient's speech. This paper presents a tool to extract phonological posteriors directly from speech signals. The proposed method consists of a bank of parallel bidirectional recurrent neural networks to estimate the posterior probabilities of the occurrence of different phonological classes. The proposed models are able to detect the phonological classes with accuracies over 90%. In addition, the trained models are available to be used by the research community interested in the topic.
Created from Fastlane, Scopus look-up
Keywords: Gated recurrent units; Pathological speech; Phonological posteriors; Recurrent neural network

Photoluminescence for Defect Detection on Full-Sized Photovoltaic Modules - Doll, Bernd; Hepp, Johannes; Hoffmann, Mathis; Schuler, Rene; Buerhop-Lutz, Claudia; Peters, Ian Marius; Hauch, Jens; Maier, Andreas; Brabec, Christoph (IEEE Journal of Photovoltaics, 2021)
Cost-effective, fast, and nondestructive on-site characterization of photovoltaic plants is required to determine countermeasures against power loss, defects, or safety problems. Methods with small impact on the operation and a high throughput, such as infrared thermography (IR), or methods with high resolution for detailed defect information, such as electroluminescence (EL) imaging, are expedient. To combine high resolution and high throughput, we propose to use photoluminescence (PL) as an outdoor characterization method for full-sized module imaging. With PL imaging as with IR imaging, no electrical contact is necessary, yet image resolution is on par with EL images. Our outdoor PL setup features an excitation source with 18 broadband, white, high power, chip on board LEDs coupled with low-cost short pass filters. This setup is suitable for indium gallium arsenide and silicon detectors. Here, we compare the visibility of common defects, including short-circuited bypass diodes, cracks, potential induced degradation, snail trails, ethylene-vinyl acetate degradation, and interconnection failures, in PL images to that in state-of-the-art imaging techniques IR, EL, and ultraviolet fluorescence. We find that out of these seven defects, five can be detected well, cell cracks under certain conditions, and interconnection failures not at all. We discuss how different techniques are complementary to enable better defect detection.
CRIS-Team Scopus Importer:2021-09-17
Keywords: Cameras; Defect detection; Detectors; fluorescence; Image resolution; Imaging; luminescence; photoluminescence; photovoltaic (PV) systems; Silicon; silicon (Si); Throughput; Tools

Photoreceptor Layer Thickness Changes During Dark Adaptation Observed With Ultrahigh-Resolution Optical Coherence Tomography - Lu, Chen D.; Lee, Byungkun; Schottenhamml, Julia; Maier, Andreas; Pugh, Edward N.; Fujimoto, James G. (Investigative Ophthalmology & Visual Science, 2017)
UnivIS-Import:2018-09-11:Pub.2017.tech.IMMD.IMMD5.photor
Keywords: Dark adaptation; Optical coherence tomography; Outer segments; Photoreceptors

Physical Constraints for Beam Hardening Reduction using Polynomial Models - Würfl, Tobias; Maass, Nicole; Dennerlein, Frank; Aichert, André; Maier, Andreas (Proceedings of the Fifth International Conference on Image Formation in X-Ray Computed Tomography (CT-Meeting), 2018)
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.physic

Physics-driven learning of x-ray skin dose distribution in interventional procedures - Roser, Philipp; Zhong, Xia; Birkhold, Annette; Strobel, Norbert; Kowarschik, Markus; Fahrig, Rebecca; Maier, Andreas (Medical Physics, 2019)
Purpose: Radiation doses accumulated during very complicated image-guided x-ray procedures have the potential to cause stochastic, but also deterministic effects, such as skin rashes or even hair loss. To monitor and reduce radiation-related risks to patients’ skin, x-ray imaging devices are equipped with online air kerma monitoring components. Traditionally, such measurements have been used to estimate skin entrance dose by (a) estimating air kerma at the interventional reference point (IRP), (b) forward projecting the dose distribution, and (c) considering a backscatter factor among other correction factors. Unfortunately, the complicated interaction between incident x-ray photons, secondary electrons, and skin tissue cannot be properly accounted for by assuming a linear relationship between forward projected air kerma and a backscatter factor. Gold standard skin dose models are therefore determined using Monte Carlo (MC) techniques. However, MC simulations are computationally complex in general and possible acceleration mainly depends on the employed hardware and variance reduction techniques. To obtain reliable and fast dose estimates, we propose to combine MC-based simulations with learning-based methods. Methods: The basic idea of our method is to approximate the radiation physics to calculate a first-order exposure estimate quickly. This initial estimate is then refined using prior knowledge derived from MC simulations. To this end, the primary photon propagation inside a voxelized patient model is estimated using a less accurate but fast photon ray casting (RC) simulation based on the Beer–Lambert law. The results of the RC simulation are then fed into a convolutional neural network (CNN), which maps the propagation of primary photons to the dose deposition inside the patient model. Additionally, the patient model itself including anatomy and material properties, such as mass density and mass energy-absorption coefficients, are fed into the CNN as well. The CNN is trained using smoothed results of MC simulations as output and RC simulations of identical imaging settings and patient models as input. Results: In total, 163 MC and associated RC simulations are carried out for the head, thorax, abdomen, and pelvis in three different voxel phantoms. We used 1e8 or 1e9 primarily emitted photons sampled from a 125 kV peak voltage spectrum, respectively. Edge-preserving smoothing (EPS) is applied to reduce (a) general stochastic uncertainties and (b) stochastic uncertainty concerning MC simulations of less primary photons. The CNN is trained using seven imaging settings of the abdomen in a single phantom. Testing its performance on the remaining datasets, the CNN is capable of estimating skin dose with an error of below 10% for the majority of test cases. Conclusion: The combination of deep neural networks and MC simulation of particle physics has the potential to decrease the computational complexity of accurate skin dose estimation. The proposed approach can provide dose distributions in under one second when running on high-end hardware. On lower cost hardware, it took up to 2 min to arrive at the same result. This makes our approach applicable in high-end environments as well as in budget solutions. Furthermore, the number of primary photons only affects the training time, while the execution time is independent of the number of primary photons.<br
Keywords: convolutional neural network; dose estimation; interventional X-ray imaging; Monte Carlo simulation

Physics-Informed Conditional Autoencoder Approach for Robust Metabolic CEST MRI at 7T - Rajput, Junaid R.; Möhle, Tim A.; Fabian, Moritz; Mennecke, Angelika; Sembill, Jochen; Kuramatsu, Joji; Schmidt, Manuel; Dörfler, Arnd; Maier, Andreas; Zaiß, Moritz (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2023)
Chemical exchange saturation transfer (CEST) is an MRI method that provides insights on the metabolic level. Several metabolite effects appear in the CEST spectrum. These effects are isolated by Lorentzian curve fitting. The separation of CEST effects suffers from the inhomogeneity of the saturation field B1. This leads to inhomogeneities in the associated metabolic maps. Current B1 correction methods require at least two sets of CEST-spectra. This at least doubles the acquisition time. In this study, we investigated the use of an unsupervised physics-informed conditional autoencoder (PICAE) to efficiently correct B1 inhomogeneity and isolate metabolic maps while using a single CEST scan. The proposed approach integrates conventional Lorentzian model into the conditional autoencoder and performs voxel-wise B1 correction and Lorentzian line fitting. The method provides clear interpretation of each step and is inherently generative. Thus, CEST-spectra and fitted metabolic maps can be created at arbitrary B1 levels. This is important because the B1 dispersion contains information about the exchange rates and concentration of metabolite protons, paving the way for their quantification. The isolated maps for tumor data showed a robust B1 correction and more than 25% increase in structural similarity index (SSIM) with gadolinium reference image compared to the standard interpolation-based method and subsequent Lorentzian curve fitting. This efficient correction method directly results in at least 50% reduction in scan time.
CRIS-Team Scopus Importer:2023-11-03
Keywords: Acquisition time; Bound loss; CEST; Interpretability; Lorentzian fit; Physics-informed autoencoder

Pitfalls in interventional X-ray organ dose assessment—combined experimental and computational phantom study: application to prostatic artery embolization - Roser, Philipp; Birkhold, Annette; Zhong, Xia; Ochs, Philipp; Stepina, Elizaveta; Kowarschik, Markus; Fahrig, Rebecca; Maier, Andreas (International Journal of Computer Assisted Radiology and Surgery, 2019)
Purpose With X-ray radiation protection and dose management constantly gaining interest in interventional radiology, novel procedures often undergo prospective dose studies using anthropomorphic phantoms to determine expected reference organ-equivalent dose values. Due to inherent uncertainties, such as impact of exact patient positioning, generalized geometry of the phantoms, limited dosimeter positioning options, and composition of tissue-equivalent materials, these dose values might not allow for patient-specific risk assessment. Therefore, first the aim of this study is to quantify the influence of these parameters on local X-ray dose to evaluate their relevance in the assessment of patient-specific organ doses. Second, this knowledge further enables validating a simulation approach, which allows employing physiological material models and patient-specific geometries. Methods Phantom dosimetry experiments using MOSFET dosimeters were conducted reproducing imaging scenarios in prostatic arterial embolization (PAE). Associated organ-equivalent dose of prostate, bladder, colon, and skin was determined. Dose deviation induced by possible small displacements of the patient was reproduced by moving the X-ray source. Dose deviation induced by geometric and material differences was investigated by analyzing two different commonly used phantoms. We reconstructed the experiments using Monte Carlo (MC) simulations, a reference male geometry, and different material properties to validate simulations and experiments against each other. Results Overall, MC-simulated organ dose values are in accordance with the measured ones for the majority of cases. Marginal displacements of X-ray source relative to the phantoms lead to deviations of 6–135% in organ dose values, while skin dose remains relatively constant. Regarding the impact of phantom material composition, underestimation of internal organ dose values by 12–20% is prevalent in all simulated phantoms. Skin dose, however, can be estimated with low deviation of 1–8% at least for two materials. Conclusions Prospective reference dose studies might not extend to precise patient-specific dose assessment. Therefore, online organ dose assessment tools, based on advanced patient modeling and MC methods, are desirabl
Keywords: Anthropomorphic phantom; Dosimetry; MOSFET; Monte Carlo simulation; Prostatic artery embolization

Pixel-wise Distance Regression for Glacier Calving Front Detection and Segmentation - Davari, Amirabbas; Baller, Christoph; Seehaus, Thorsten; Braun, Matthias; Maier, Andreas; Christlein, Vincent (IEEE Transactions on Geoscience and Remote Sensing, 2022)
Glacier calving front position (CFP) is an important glaciological variable. Traditionally, delineating the CFPs has been carried out manually, which was subjective, tedious and expensive. Automating this process is crucial for continuously monitoring the evolution and status of glaciers. Recently, deep learning approaches have been investigated for this application. However, the current methods get challenged by a severe class-imbalance problem. In this work, we propose to mitigate the class-imbalance between the calving front class and the non-calving front class by reformulating the segmentation problem into a pixel-wise regression task. A Convolutional Neural Network gets optimized to predict the distance values to the glacier front for each pixel in the image. The resulting distance map localizes the CFP and is further post-processed to extract the calving front line. We propose three post-processing methods, one method based on statistical thresholding, a second method based on conditional random fields (CRF), and finally the use of a second U-Net. The experimental results confirm that our approach significantly outperforms the state-of-the-art methods and produces accurate delineation. The Second U-Net obtains the best performance results, resulting in an average improvement of about 21% dice coefficient enhancement.
CRIS-Team Scopus Importer:2022-03-25
Keywords: class-imbalance; distance map regression; glacier calving front segmentation; SAR imagery; U-Net

Pleural Effusion Classification on Chest X-Ray Images with Contrastive Learning - Zeiser, Felipe André; Santos, Ismael Garcia; Bohn, Henrique Christoph; da Costa, Cristiano André; de Oliveira Ramos, Gabriel; da Rosa Righi, Rodrigo; Maier, Andreas; Andrade, José Rodrigo Mendes; Bacelar, Alexandre (International Conference on Web Information Systems and Technologies, WEBIST - Proceedings, 2023)
Diagnosing pleural effusion is important to recognize the disease's etiology and reduce the length of hospital stay for patients after fluid content analysis. In this context, machine learning techniques have been increasingly used to help physicians identify radiological findings. In this work, we propose using contrastive learning to classify chest X-rays with and without pleural effusion. A model based on contrastive learning is trained to extract discriminative features from the images and reports to maximize the similarity between the correct image and text pairs. Preliminary results show that the proposed approach is promising, achieving an AUC of 0.900, an accuracy of 86.28%, and a sensitivity of 88.54% for classifying pleural effusion on chest X-rays. These results demonstrate that the proposed method achieves comparable or superior to state of the art results. Using contrastive learning can be a promising alternative to improve the accuracy of medical image classification models, contributing to a more accurate and effective diagnosis.
CRIS-Team Scopus Importer:2023-12-22
Keywords: Contrastive Learning; Pleural Effusion; X-Ray

PLIKS: A Pseudo-Linear Inverse Kinematic Solver for 3D Human Body Estimation - Shetty, Karthik; Birkhold, Annette; Jaganathan, Srikrishna; Strobel, Norbert; Kowarschik, Markus; Maier, Andreas; Egger, Bernhard (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2023)
We introduce PLIKS (Pseudo-Linear Inverse Kinematic Solver) for reconstruction of a 3D mesh of the human body from a single 2D image. Current techniques directly regress the shape, pose, and translation of a parametric model from an input image through a non-linear mapping with minimal flexibility to any external influences. We approach the task as a model-in-the-loop optimization problem. PLIKS is built on a linearized formulation of the parametric SMPL model. Using PLIKS, we can analytically reconstruct the human model via 2D pixel-aligned vertices. This enables us with the flexibility to use accurate camera calibration information when available. PLIKS offers an easy way to introduce additional constraints such as shape and translation. We present quantitative evaluations which confirm that PLIKS achieves more accurate reconstruction with greater than 10% improvement compared to other state-of-the-art methods with respect to the standard 3D human pose and shape benchmarks while also obtaining a reconstruction error improvement of 12.9 mm on the newer AGORA dataset.
CRIS-Team Scopus Importer:2023-11-17
Keywords: 3D from single images

PMS-GAN: Parallel Multi-Stream Generative Adversarial Network for Multi-Material Decomposition in Spectral Computed Tomography - Geng, Mufeng; Tian, Zifeng; Jiang, Zhe; You, Yunfei; Feng, Ximeng; Xia, Yan; Yang, Kun; Ren, Qiushi; Meng, Xiangxi; Maier, Andreas; Lu, Yanye (IEEE Transactions on Medical Imaging, 2021)
Spectral computed tomography is able to provide quantitative information on the scanned object and enables material decomposition. Traditional projection-based material decomposition methods suffer from the nonlinearity of the imaging system, which limits the decomposition accuracy. Inspired by the generative adversarial network, we proposed a novel parallel multi-stream generative adversarial network (PMS-GAN) to perform projection-based multi-material decomposition in spectral computed tomography. By designing the differential map and incorporating the adversarial network into loss function, the decomposition accuracy was significantly improved with robust performance. The proposed network was quantitatively evaluated by both simulation and experimental study. The results show that PMS-GAN outperformed the reference methods with certain robustness. Compared with Pix2pix-GAN, PMS-GAN increased the structural similarity index by 172% on the contrast agent Ultravist370, 11% on bones, and 71% on bone marrow, respectively, in a simulated test scenario. In an experimental test scenario, 9% and 38% improvements of the structural similarity index on the biopsy needle and on a torso phantom were observed, respectively. The proposed network demonstrates its capability of multi-material decomposition and has certain potential toward clinical applications.
CRIS-Team WoS Importer:2021-03-19

PoCaP Corpus: A Multimodal Dataset for Smart Operating Room Speech Assistant Using Interventional Radiology Workflow Analysis - Demir, Kubilay Can; May, Matthias; Schmid, Axel; Uder, Michael; Breininger, Katharina; Weise, Tobias; Maier, Andreas; Yang, Seung Hee (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2022)
This paper presents a new multimodal interventional radiology dataset, called PoCaP (Port Catheter Placement) Corpus. This corpus consists of speech and audio signals in German, X-ray images, and system commands collected from 31 PoCaP interventions by six surgeons with average duration of 81.4 ± 41.0 min. The corpus aims to provide a resource for developing a smart speech assistant in operating rooms. In particular, it may be used to develop a speech-controlled system that enables surgeons to control the operation parameters such as C-arm movements and table positions. In order to record the dataset, we acquired consent by the institutional review board and workers’ council in the University Hospital Erlangen and by the patients for data privacy. We describe the recording set-up, data structure, workflow and preprocessing steps, and report the first PoCaP Corpus speech recognition analysis results with 11.52% word error rate using pretrained models. The findings suggest that the data has the potential to build a robust command recognition system and will allow the development of a novel intervention support systems using speech and image processing in the medical domain.
CRIS-Team Scopus Importer:2022-10-14
Keywords: Automatic speech recognition; Interventional radiology; Multimodal interventional corpus; Operating room smart assistant; Port catheter placement; Surgical data science

PoCaPNet: A Novel Approach for Surgical Phase Recognition Using Speech and X-Ray Images - Yang, Seung Hee; Demir, Kubilay Can; Weise, Tobias; Schmid, Axel; May, Matthias; Maier, Andreas (International Conference Interspeech 2023, 2023)
Surgical phase recognition is a challenging and necessary task for the development of context-aware intelligent systems that can support medical personnel for better patient care and effective operating room management. In this paper, we present a surgical phase recognition framework that employs a Multi-Stage Temporal Convolution Network using speech and X-Ray images for the first time. We evaluate our proposed approach using our dataset that comprises 31 port-catheter placement operations and report 82.56 % frame-wise accuracy with eight surgical phases. Additionally, we investigate the design choices in the temporal model and solutions for the class-imbalance problem. Our experiments demonstrate that speech and X-Ray data can be effectively utilized for surgical phase recognition, providing a foundation for the development of speech assistants in operating rooms of the future.
Keywords: surgical workflow; surgical phase recognition; speech assistant; port-catheter placement; TCN

Point-based Registration of Vascular Structures: Applications for Diagnostic and Interventional Procedures - Bayer, Siming (2022)
With the introduction of computer-aided methods for diagnosis and intervention, patient outcomes of many clinical procedures have been increased tremendously in the last decades. An essential task thereby is the registration of inter- or intrapatient images that are acquired using a single or multiple imaging modalities. In principle, vasculature permeates through all organs of human body. As it is interbedded spatially, it reflects the pathological changes of the surrounding tissue. Thus, accurate and robust registration methods to align vessel structures or images have the capability to benefit a large variety of clinical procedures. Numerous methods that propose novel vessel registration algorithms have been published to date. Both intensity and feature-based methods are proposed, where a clear dominance of feature-based, especially point-based methods, can be derived from the summary of the state-of-the-art. Although the methods proposed in the literature demonstrate promising results for dedicated clinical set up, a generalized approach for vessel registration regardless of region of interest has not been proposed and evaluated. Furthermore, most state-of-the-art methods requires cumbersome hyperparameter tuning, which reduce their clinical applicability consequently. Therefore, a primary goal of this thesis is to develop, implement and evaluate an accurate and inherent robust vessel registration framework with the generalizability and applicability to various clinical applications. Recent progress in machine learning and deep learning opens new perspectives to improve the efficiency of the conventional registration methods for vasculature. Promising registration results have been achieved with different learning paradigms, such as reinforcement learning, supervised- and unsupervised learning with synthetic and clinical data. Hence, a secondary goal of this thesis is to investigate the potential of other machine learning and deep learning techniques to resolve vessel image registration problems. One of the two point-based registration frameworks investigated in this thesis utilizes mixture models to align the centerlines of vasculature. Thereby, a hybrid mixture model is proposed as a key part of the framework, that models the spatial and topological information of the vasculature simultaneously and is consequently equipped with significant discriminative capacity. Moreover, an automatic refinement mechanism to identify regions with missing data is formulated. The final transformation to the target image can be estimated with different methods such as Thin-Plate-Spline, B-Spline or Gaussian Process. The evaluation with synthetic, phantom and clinical data acquired from different clinical procedures demonstrate the accuracy and inherent robustness of the entire vessel registration framework regardless of clinical set up. The other approach formulated in this thesis makes use of imitation learning paradigm to overcome the weakness and challenges of adopting reinforcement learning for image registration problem. Under the guidance of a demonstrator an agent is trained to find the optimal displacement of landmarks. The network architecture is inspired by PointNet that is able to consume raw point data as input. The proposed framework is evaluated with clinical fundoscopic images in a retrospective manner. A special attention of experiments conducted is to understand the principles of our imitation network, where the influences of the model parameters are analyzed in detail. The evaluation results demonstrate the potentials and effectiveness of the imitation network to register vascular images for the first time.<br

Polychromatic Maximum Likelihood Reconstruction for Talbot-Lau X-ray Tomography - Schiffers, Florian; Käppler, Sebastian; Pelzer, Georg; Wolf, Andreas; Maier, Andreas; Anton, Gisela; Riess, Christian (International Conference on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine (Fully3D), 2017)

Polypoidal Choroidal Vasculopathy on Swept-Source Optical Coherence Tomography Angiography with Variable Interscan Time Analysis - Rebhun, Carl B.; Moult, Eric M.; Novais, Eduardo A.; Moreira-Neto, Carlos; Ploner, Stefan; Louzada, Ricardo N.; Lee, Byungkun; Baumal, Caroline R.; Fujimoto, James G.; Duker, Jay S.; Waheed, Nadia K.; Ferrara, Daniela (Translational Vision Science & Technology, 2017)
Purpose: To use a novel optical coherence tomography angiography (OCTA) algorithm termed variable interscan time analysis (VISTA) to evaluate relative blood flow speeds in polypoidal choroidal vasculopathy (PCV). Methods: Prospective cross-sectional study enrolling patients with confirmed diagnosis of PCV. OCTA of the retina and choroid was obtained with a prototype swept-source OCT system. The acquired OCT volumes were centered on the branching vascular network (BVN) and polyps as determined by indocyanine-green angiography (ICGA). The relative blood flow speeds were characterized on VISTAOCTA. Results: Seven eyes from seven patients were evaluated. Swept-source OCTA enabled detailed enface visualization of the BVN and polyps in six eyes. VISTA-OCTA revealed variable blood flow speeds in different PCV lesion components of the same eye, with faster flow in the periphery of polyps and slower flow in the center of each polyp, as well as relatively slow flow in BVN when compared with retinal vessels. BVNs demonstrated relatively faster blood flow speeds in the larger trunk vessels and relatively slower speeds in the smaller vessels. Conclusions: Swept-source OCTA identifies polyps in most, but not all, PCV lesions. This limitation that may be related to relatively slow blood flow within the polyp, which may be below the OCTA’s sensitivity. VISTA-OCTA showed heterogeneous blood flow speeds within the polyps, which may indicate turbulent flow in the polyps. Translational Relevance: These results bring relevant insights into disease mechanisms that can account for the variable course of PCV, and can be relevant for diagnosis and management of patients with PCV.
UnivIS-Import:2018-09-05:Pub.2017.tech.IMMD.IMMD5.polypo
Keywords: polypoidal choroidal vasculopathy; pcv; oct; octa; vista

Portability of TV-Regularized Reconstruction Parameters to Varying Data Sets - Amrehn, Mario; Maier, Andreas; Dennerlein, Frank; Hornegger, Joachim (Bildverarbeitung für die Medizin 2015, 2015)
In C-arm computed tomography there are certain constraints due to the data acquisition process which can cause limited raw data. The reconstructed image’s quality may significantly decrease depending on these constraints. To compensate for severely under-sampled projection data during reconstruction, special algorithms have to be utilized, more robust to such ill-posed problems. In the past few years it has been shown that reconstruction algorithms based on the theory of compressed sensing are able to handle incomplete data sets quite well. In this paper, the iterative iTV reconstruction method by Ludwig Ritschl et al. is analyzed regarding it’s elimination capabilities of image artifacts caused by incomplete raw data with respect to the settings of it’s various parameters. The evaluation of iTV and the data dependency of iterative reconstruction’s parameters is conducted in two stages. First, projection data with severe angular under-sampling is acquired using an analytical phantom. Proper reconstruction parameters are selected by analyzing the reconstruction results from a set of proposed parameters. In a second step multiple phantom data sets are acquired with limited angle geometry and a small number of projections. The iTV reconstructions of these data sets are compared to short-scan FDK and SART reconstruction results, highlighting the distinct data dependence of the iTV reconstruction parameters.
UnivIS-Import:2018-09-06:Pub.2015.tech.IMMD.IMMD5.portab_4
Keywords: health; medicine; image processing; reconstruction; segmentation; pattern recognition; machine learning

Possible Impact of functional active GPCR-autoantibodies on retinal microcirculation in Long-COVID - Mardin, Christian Y.; Lucio, Marianna; Wallukat, Gerd; Szewczykowski, Charlotte; Hoffmanns, Jacob; Raith, Franziska; Schröder, Tim; Schottenhamml, Julia; Lämmer, Robert; Hohberger, Bettina (INVESTIGATIVE OPHTHALMOLOGY & VISUAL SCIENCE, 2022)
CRIS-Team WoS Importer:2022-09-30

Precision Learning: Reconstruction Filter Kernel Discretization - Syben-Leisner, Christopher; Stimpel, Bernhard; Breininger, Katharina; Würfl, Tobias; Fahrig, Rebecca; Dörfler, Arnd; Maier, Andreas (Proceedings of the 5th International Conference on Image Formation in X-ray Computed Tomography, 2018)
UnivIS-Import:2018-09-11:Pub.2018.tech.IMMD.IMMD5.precis_0

Precision Learning: Towards Use of Known Operators in Neural Networks - Maier, Andreas; Schebesch, Frank; Syben-Leisner, Christopher; Würfl, Tobias; Steidl, Stefan; Choi, Jang-Hwan; Fahrig, Rebecca (2018 24rd International Conference on Pattern Recognition (ICPR), 2018)
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.precis_5

Predicting Anxiety in Routine Palliative Care Using Bayesian-Inspired Association Rule Mining - Haas, Oliver; Lopera Gonzalez, Luis Ignacio; Hofmann, Sonja; Ostgathe, Christoph; Maier, Andreas; Rothgang, Eva; Amft, Oliver; Steigleder, Tobias (Frontiers in Digital Health, 2021)
We propose a novel knowledge extraction method based on Bayesian-inspired association rule mining to classify anxiety in heterogeneous, routinely collected data from 9,924 palliative patients. The method extracts association rules mined using lift and local support as selection criteria. The extracted rules are used to assess the maximum evidence supporting and rejecting anxiety for each patient in the test set. We evaluated the predictive accuracy by calculating the area under the receiver operating characteristic curve (AUC). The evaluation produced an AUC of 0.89 and a set of 55 atomic rules with one item in the premise and the conclusion, respectively. The selected rules include variables like pain, nausea, and various medications. Our method outperforms the previous state of the art (AUC = 0.72). We analyzed the relevance and novelty of the mined rules. Palliative experts were asked about the correlation between variables in the data set and anxiety. By comparing expert answers with the retrieved rules, we grouped rules into expected and unexpected ones and found several rules for which experts' opinions and the data-backed rules differ, most notably with the patients' sex. The proposed method offers a novel way to predict anxiety in palliative settings using routinely collected data with an explainable and effective model based on Bayesian-inspired association rule mining. The extracted rules give further insight into potential knowledge gaps in the palliative care field.
Keywords: anxiety; machine learning; association rule mining; palliative care; routine data; questionnaire

Predicting Hearing Aid Fittings Based on Audiometric and Subject-Related Data: A Machine Learning Approach - Seifer, Ann-Kristin; Schinkel-Bielefeld, Nadja; Schröter, Hendrik; Escalante Banuelos, Alberto; Hoppe, Ulrich; Maier, Andreas (Virtual Conference on Computational Audiology (VCCA2020), 2020)
Hearing aids (HA) are configured to the wearer’s individual needs, which might vary greatly from user to user. Currently, it is common practice, that the initial HA gain settings are based on generic fitting formulas that link a user’s pure-tone hearing threshold to amplification characteristics. Subsequently, a time-consuming fine-tuning process follows, in which a hearing care professional (HCP) adjusts the HA settings to the user’s individual demands. An advanced, more personalized gain prescription procedure could support HCPs by reducing fine-tuning effort and facilitate over-the-counter HAs. We propose a machine learning based prediction for HA gain to minimize subsequent fine-tuning effort. The data-driven approach takes audiometic and personal variables into account, such as age, gender, and the user’s acoustical environment. A random forest regression model was trained on real-world HA fittings from the Connexx database (fitting software provided by Sivantos GmbH). Three months of data from Connexx version 9.1.0.364 were used. A data cleaning framework was implemented to extract a representative data set based on a list of machine learning and audiological criteria. These criteria include, for instance, using only ‘informative’ HCPs who perform fine-tuning for at least some patients. Furthermore, ‘informative’ HCPs are those who perform diagnostics beyond air conduction audiograms, use new technologies and special features. The resulting training data comprised 20,000 HA fittings and a 10-fold cross validation was used to train the random forest.
Keywords: Hearing Aids; Machine Learning

Prediction of Household-level Heat-Consumption using PSO enhanced SVR Model - Chatterjee, Satyaki; Bayer, Siming; Maier, Andreas (Tackling Climate Change with Machine Learning: workshop at NeurIPS 2021, 2021)
In combating climate change, an effective demand-based energy supply operation of the district energy system (DES) for heating or cooling is indispensable. As a consequence, an accurate forecast of heat consumption on the consumer side poses an important first step towards an optimal energy supply. However, due to the non-linearity and non-stationarity of heat consumption data, the prediction of the thermal energy demand of DES remains challenging. In this work, we propose a forecasting framework for thermal energy consumption within a district heating system (DHS) based on kernel Support Vector Regression (kSVR) using real-world smart meter data. Particle Swarm Optimization (PSO) is employed to find the optimal hyper-parameter for the kSVR model which leads to the superiority of the proposed methods when compared to a state-of-the-art ARIMA model. The average MAPE is reduced to 2.07% and 2.64% for the individual meter-specific forecasting and for forecasting of societal consumption, respectivel

Prediction of MRI Hardware Failures based on Image Features using Ensemble Learning - Kuhnert, Nadine; Pflueger, Lea; Maier, Andreas (Bildverarbeitung für die Medizin 2020, 2020)
In order to ensure trouble-free operation, prediction of hardware failures is essential. This applies especially to medical systems. Our goal is to determine hardware which needs to be exchanged before failing. In this work, we focus on predicting failures of head/neck coils using image-related measurements. Thus, we aim to solve a classification problem with two classes, normal and broken coil. To solve this problem, we use data of two different levels. One level refers to one-dimensional features per individual coil channel on which we found a fully connected neural network to perform best. Furthermore, we use matrix features representing the state of an entire coil to train another neural network on. This ensemble of two networks and combining them using a Random Forest improves the prediction results even further. Thus, combining insights of both trained models allows us to determine the coil's condition with an F-score of 94.14% and an accuracy of 99.09%.

Prediction of MRI Hardware Failures based on Image Features using Time Series Classification - Kuhnert, Nadine; Pflueger, Lea; Maier, Andreas (Bildverarbeitung für die Medizin, 2020)
Already before systems malfunction one has to know if hardware components will fail in near future in order to counteract in time. Thus, unplanned downtime is ought to be avoided. In medical imaging, maximizing the system's uptime is crucial for patients' health and healthcare provider's daily business. We aim to predict failures of Head/Neck coils used in Magnetic Resonance Imaging (MRI) by training a statistical model on sequential data collected over time. As image features depend on the coil's condition, their deviations from the normal range already hint to future failure. Thus, we used image features and their variation over time to predict coil damage. After comparison of different time series classification methods we found Long Short Term Memorys (LSTMs) to achieve the highest F-score of 86.43% and to tell with 98.33% accuracy if hardware should be replaced.
Keywords: Time Series Classification; LSTM; Classification; Time Series Prediction

Prediction of Respiration-Induced Internal 3-D Deformation Fields From Dense External 3-D Surface Motion - Taubmann, Oliver; Wasza, Jakob; Forman, Christoph; Fischer, Peter; Wetzl, Jens; Maier, Andreas; Hornegger, Joachim (Proceedings of the 28th International Congress and Exhibition, 2014)
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.predic_9
Keywords: Respiratory Motion; Motion Modeling; Motion Prediction; External Surrogate

Prediction of Stress Coping Capabilities from Nightly Heart Rate Patterns using Machine Learning - Vorberg, Linda; Pflüger, Siri; Richer, Robert; Jäger, Katharina; Küderle, Arne; Rohleder, Nicolas; Eskofier, Björn (2023 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI), 2023)
Stress is related to short- and long-term alterations in stress systems, including the hypothalamic-pituitary-adrenal (HPA) axis and the sympathetic nervous system (SNS). While it is well established that stress experienced during the day can affect sleep quality, less is known about how it affects stress systems during the night. We assume that stress coping strategies can have an impact on how stress carries over into the night and that individuals with bad coping mechanisms show elevated activation of stress systems during sleep. For that reason, we recorded the heart rate (HR) and heart rate variability (HRV) of 21 healthy participants on two consecutive nights during sleep and the first hour after awakening and extracted cortisol and alpha-amylase from saliva samples collected in the first hour after awakening. Stress coping capabilities were assessed using self-reports. We performed backward stepwise multiple regression models to analyze the relationship between HR(V) and stress coping and trained different machine learning-based regression algorithms to predict positive (SVF Pos ) and negative (SVF Neg ) stress coping capabilities, respectively. Our results show that individuals with higher SVF Neg scores showed higher SNS activity during the night, whereas higher SVF Pos scores indicated lower SNS activity. SVF Pos was predicted with a mean absolute error (MAE) of 1.51 ± 0.73 and SVF Neg with an MAE of 2.79 ± 1.53. Our findings indicate an association between nightly HR(V) and the individual’s capability of coping with stress. This provides further information about how stress influences sleep and might be used for tailored intervention and feedback on successful stress coping.<br
Keywords: Analytical models; Machine learning algorithms; Machine learning; Predictive models; Prediction algorithms; Sympathetic nervous system; Heart rate variability; stress coping; sleep; cortisol awakening response; heart rate variability; machine learning

Prediction of the Benefit of Motion-Compensated Reconstruction for Whole-Heart Coronary MRI - Wetzl, Jens; Forman, Christoph; Maier, Andreas; Hornegger, Joachim; Zenge, Michael O. (Proceedings of the 23rd Annual Meeting of the ISMRM (ISMRM 2015), 2015)
UnivIS-Import:2015-07-08:Pub.2015.tech.IMMD.IMMD5.predic

Predictive coding and stochastic resonance as fundamental principles of auditory phantom perception - Schilling, Achim; Sedley, William; Gerum, Richard C.; Metzner, Claus; Tziridis, Konstantin; Maier, Andreas; Schulze, Holger; Zeng, Fan Gang; Friston, Karl J.; Krauß, Patrick (Brain, 2023)
Mechanistic insight is achieved only when experiments are employed to test formal or computational models. Furthermore, in analogy to lesion studies, phantom perception may serve as a vehicle to understand the fundamental processing principles underlying healthy auditory perception. With a special focus on tinnitus-as the prime example of auditory phantom perception-we review recent work at the intersection of artificial intelligence, psychology and neuroscience. In particular, we discuss why everyone with tinnitus suffers from (at least hidden) hearing loss, but not everyone with hearing loss suffers from tinnitus. We argue that intrinsic neural noise is generated and amplified along the auditory pathway as a compensatory mechanism to restore normal hearing based on adaptive stochastic resonance. The neural noise increase can then be misinterpreted as auditory input and perceived as tinnitus. This mechanism can be formalized in the Bayesian brain framework, where the percept (posterior) assimilates a prior prediction (brain's expectations) and likelihood (bottom-up neural signal). A higher mean and lower variance (i.e. enhanced precision) of the likelihood shifts the posterior, evincing a misinterpretation of sensory evidence, which may be further confounded by plastic changes in the brain that underwrite prior predictions. Hence, two fundamental processing principles provide the most explanatory power for the emergence of auditory phantom perceptions: predictive coding as a top-down and adaptive stochastic resonance as a complementary bottom-up mechanism. We conclude that both principles also play a crucial role in healthy auditory perception. Finally, in the context of neuroscience-inspired artificial intelligence, both processing principles may serve to improve contemporary machine learning techniques.
CRIS-Team Scopus Importer:2023-12-15
Keywords: artificial intelligence; Bayesian brain; phantom perception; predictive coding; stochastic resonance; tinnitus

Preface Vorwort - Maier, Andreas; Deserno, Thomas; Handels, Heinz; Maier-Hain, Klaus; Palm, Christoph; Tolxdorff, Thomas (Informatik aktuell, 2018)
CRIS-Team Scopus Importer:2022-06-05

Preliminary Study Investigating Brain Shift Compensation using 3D CBCT Cerebral Vascular Images - Bayer, Siming; Schaffert, Roman; Ravikumar, Nishant; Maier, Andreas; Fahrig, Rebecca; Ostermeier, Martin; Tong, Xiaoguang; Wang, Hu (Bildverarbeitung für die Medizin 2018, 2018)
UnivIS-Import:2018-09-11:Pub.2018.tech.IMMD.IMMD5.prelim_3

Prior-based automatic segmentation of the carotid artery lumen in TOF MRA (PASCAL) - Hutter, Jana; Hofmann, Hannes; Grimm, Robert; Greiser, Andreas; Saake, Marc; Hornegger, Joachim; Dörfler, Arnd; Schmitt, Peter (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012)
In current clinical practice, examinations of the carotid artery bifurcation are commonly carried out with Computed Tomography Angiography (CTA) or contrast-enhanced Magnetic Resonance Angiography (ceMRA). Quantitative information about vessel morphology, extracted from segmentations, is promising for diagnosis of vessel pathologies. However, both above-mentioned techniques require the administration of contrast media. In contrary, non-ce MRA methods such as Time-of-Flight (TOF) provide fully non-invasive imaging without any exogenous contrast agent. The diagnostic value of TOF MRA, however, for assessment of the carotid bifurcation area can be hampered due to its susceptibility to irregular blood flow patterns. Conventional methods for lumen segmentation are very sensitive to such signal voids and produce inaccurate results. In this work, a novel, fully automatic 3D segmentation algorithm is proposed which uses prior knowledge about irregular flow patterns. The presented technique has been successfully tested on eleven volunteer datasets as well as in a patient case, offering the comparison to CTA images. The sensitivity could be increased by 29.2% to 85.6% compared to standard level set methods. The root mean squared error in diameter measurements was reduced from 4.85mm to 1.44 mm.
CRIS-Team Scopus Importer:2022-08-05

Prior-based multi material decomposition (PBMMD) for dual energy CT - Dorn, Sabrina; Chen, Shuqing; Sawall, Stefan; Maier, Joscha; Knaup, Michael; Maier, Andreas; Lell, Michael; Kachelriess, Marc (the fifth edition of The International Conference on Image Formation in X-ray Computed Tomography, 2018)
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.priorb

Prior-Free Respiratory Motion Estimation in Rotational Angiography - Unberath, Mathias; Taubmann, Oliver; Aichert, André; Achenbach, Stephan; Maier, Andreas (IEEE Transactions on Medical Imaging, 2018)
Rotational coronary angiography using C-arm angiography systems enables intra-procedural 3-D imaging that is considered beneficial for diagnostic assessment and interventional guidance. Despite previous efforts, rotational angiography was not yet successfully established in clinical practice for coronary artery procedures due to challenges associated with substantial intra-scan respiratory and cardiac motion. While gating handles cardiac motion during reconstruction, respiratory motion requires compensation. State-of-the-art algorithms rely on 3-D / 2-D registration that requires an uncompensated reconstruction of sufficient quality. To overcome this limitation, we investigate two prior-free respiratory motion estimation methods based on the optimization of: 1) epipolar consistency conditions (ECCs) and 2) a task-based auto-focus measure (AFM). The methods assess redundancies in projection images or impose favorable properties of 3-D space, respectively, and are used to estimate the respiratory motion of the coronary arteries within rotational angiograms. We evaluate our algorithms on the publicly available CAVAREV benchmark and on clinical data. We quantify reductions in error due to respiratory motion compensation using a dedicated reconstruction domain metric. Moreover, we study the improvements in image quality when using an analytic and a novel temporal total variation regularized algebraic reconstruction algorithm. We observed substantial improvement in all figures of merit compared with the uncompensated case. Improvements in image quality presented as a reduction of double edges, blurring, and noise. Benefits of the proposed corrections were notable even in cases suffering little corruption from respiratory motion, translating to an improvement in the vessel sharpness of (6.08 ± 4.46)% and (14.7 ± 8.80)% when the ECC-based and the AFM-based compensation were applied. On the CAVAREV data, our motion compensation approach exhibits an improvement of (27.6 ± 7.5)% and (97.0 ± 17.7)% when the ECC and AFM were used, respectively. At the time of writing, our method based on AFM is leading the CAVAREV scoreboard. Both motion estimation strategies are purely image-based and accurately estimate the displacements of the coronary arteries due to respiration. While current evidence suggests the superior performance of AFM, future work will further investigate the use of ECC in the context of angiography as they solely rely on geometric calibration and projection-domain images.
EVALuna2:35976

Probe-based confocal laser endomicroscopy in detecting malignant lesions of vocal folds. - Goncalves, Miguel; Aubreville, Marc; Mueller, Sarina K.; Sievert, Matti; Maier, Andreas; Iro, Heinrich; Bohr, Christopher (Acta Otorhinolaryngologica Italica, 2019)
Probe-based confocal laser endomicroscopy (CLE) is an innovative technique for real-time, non-invasive analysis of the surface epithelium. While being successfully used for diagnosis by experts, this method has not yet been established in clinical routine, partly due to the lack of standards and criteria for classifying various lesions. Our aim was to determine the diagnostic value and inter-rater reliability of CLE in detecting malignant lesions of the vocal cords. 58 video sequences were extracted from the probe-based CLE (GastroFlex probe with a Cellvizio® laser system) examinations of 3 patients with squamous cell carcinomas and 4 patients with benign alterations of the vocal folds. Two ENT surgeons, who were blinded to the histological result, were asked to identify the sequences representing a carcinoma. We showed an accuracy, sensitivity, specificity, PPV and NPV of 91.38-96.55%, 100%, 87.8-95.2%, 77.27-89.47% and 100%, respectively, with an inter-rater reliability of k = 0.89 ("almost perfect agreement"). Probe-based CLE is a promising method for diagnosis and assessment of vocal fold lesions in vivo. Our results suggest that, with adequate training, the diagnostic value of this technique can be improved and potentially provide important information during oncological surgery.

Projection and Reconstruction-Based Noise Filtering Methods in Cone Beam CT - Lorch, Benedikt; Berger, Martin; Hornegger, Joachim; Maier, Andreas (Bildverarbeitung für die Medizin 2015, 2015)
UnivIS-Import:2015-04-17:Pub.2015.tech.IMMD.IMMD5.projec

Projection-Based Denoising Method for Photon-Counting Energy-Resolving Detectors - Lu, Yanye; Manhart, Michael; Taubmann, Oliver; Zobel, Tobias; Yang, Qiao; Choi, Jang-Hwan; Wu, Meng; Dörfler, Arnd; Fahrig, Rebecca; Ren, Qiushi; Hornegger, Joachim; Maier, Andreas (Bildverarbeitung für die Medizin 2015, 2015)
UnivIS-Import:2017-12-18:Pub.2015.tech.IMMD.IMMD5.projec_7

Projection-based Material Decomposition by Machine Learning using Image-based Features for Computed Tomography - Lu, Yanye; Geret, Jan; Unberath, Mathias; Manhart, Michael; Ren, Qiushi; Fahrig, Rebecca; Hornegger, Joachim; Maier, Andreas (The 13th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2015)
UnivIS-Import:2017-12-18:Pub.2015.tech.IMMD.IMMD5.projec_5

Projection image-to-image translation in hybrid x-ray/MR imaging - Stimpel, Bernhard; Syben-Leisner, Christopher; Würfl, Tobias; Breininger, Katharina; Lommen, Jonathan; Dörfler, Arnd; Maier, Andreas (Progress in Biomedical Optics and Imaging - Proceedings of SPIE, 2019)
The potential benefit of hybrid X-ray and MR imaging in the interventional environment is large due to the combination of fast imaging with high contrast variety. However, a vast amount of existing image enhancement methods requires the image information of both modalities to be present in the same domain. To unlock this potential, we present a solution to image-to-image translation from MR projections to corresponding x-ray projection images. The approach is based on a state-of-the-art image generator network that is modified to fit the specific application. Furthermore, we propose the inclusion of a gradient map in the loss function to allow the network to emphasize high-frequency details in image generation. Our approach is capable of creating x-ray projection images with natural appearance. Additionally, our extensions show clear improvement compared to the baseline method.
CRIS-Team Scopus Importer:2019-07-12

Projection-to-Projection Translation for Hybrid X-ray and Magnetic Resonance Imaging - Stimpel, Bernhard; Syben-Leisner, Christopher; Würfl, Tobias; Breininger, Katharina; Hölter, Philip; Dörfler, Arnd; Maier, Andreas (Scientific Reports, 2019)
Hybrid X-ray and magnetic resonance (MR) imaging promises large potential in interventional medical imaging applications due to the broad variety of contrast of MRI combined with fast imaging of X-ray-based modalities. To fully utilize the potential of the vast amount of existing image enhancement techniques, the corresponding information from both modalities must be present in the same domain. For image-guided interventional procedures, X-ray fluoroscopy has proven to be the modality of choice. Synthesizing one modality from another in this case is an ill-posed problem due to ambiguous signal and overlapping structures in projective geometry. To take on these challenges, we present a learning-based solution to MR to X-ray projection-to-projection translation. We propose an image generator network that focuses on high representation capacity in higher resolution layers to allow for accurate synthesis of fine details in the projection images. Additionally, a weighting scheme in the loss computation that favors high-frequency structures is proposed to focus on the important details and contours in projection imaging. The proposed extensions prove valuable in generating X-ray projection images with natural appearance. Our approach achieves a deviation from the ground truth of only 6% and structural similarity measure of 0.913 ± 0.005. In particular the high frequency weighting assists in generating projection images with sharp appearance and reduces erroneously synthesized fine details.<br

Projective Invariants for Geometric Calibration in Flat-Panel Computed Tomography - Aichert, André; Bier, Bastian; Rist, Leonhard; Maier, Andreas (Proceedings of the Fifth International Conference on Image Formation in X-Ray Computed Tomography (CT-Meeting), 2018)
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.projec_8

Projective Reconstruction of Acquisition Geometry without Point Correspondences - Würfl, Tobias; Maass, Nicole; Dennerlein, Frank; Maier, Andreas (Proceedings of the 6th International Conference on Image Formation in X-Ray Computed Tomography, 2020)
Despite some fundamental differences in the image formation model 3D Reconstruction in the fields of Computed Tomography and Computer Vision share some similar concepts. In Computed Tomography, reconstruction refers to the problem of estimating 3D structure from 2D projection images assuming that accurate information about the camera geometry is available while in Computer Vision reconstruction typically refers to the task of simultaneously estimating the 3D structure and the camera motion solely from 2D projection images. In Computed Tomography accurate information about the camera geometry or motion is usually acquired by calibration and scanners are manufactured to support reproducible trajectories. Being able to reconstruct without requiring calibration and a reproducible trajectory would solve the problem of rigid motion estimation and enable new types of scanners. However, it is difficult to transfer Computer Vision techniques to Computed Tomography because point correspondences are hard to establish since all structures are superimposed over each other in projection images. Recently we introduced a new algorithm two estimate pairwise relative geometry based solely on the 2D projection images without point correspondences. In this work, we present the adaptation of a structure from motion algorithm to obtain a projective reconstruction of the whole scene from pairwise geometry estimates. In a simulation experiment, we show that we are able to estimate the camera geometry only based on projections.<br

Proof of Concept: Automatic Type Recognition - Christlein, Vincent; Weichselbaumer, Nikolaus; Limbach, Saskia; Seuret, Mathias (INFORMATIK 2020, 2021)

Properties of the ellipse-line-ellipse trajectory with asymmetrical variations - Guo, Zijia; Noo, Frederic; Maier, Andreas; Lauritsch, Guenter (Medical Imaging 2016: Physics of Medical Imaging, 2016)
UnivIS-Import:2016-06-01:Pub.2016.tech.IMMD.IMMD5.proper

Prospective motion assessment within multi-shot imaging using coil mixing of the data consistency error and deep learning - Hoßbach, Julian; Splitthoff, Daniel; Clifford, Bryan; Polak, Daniel; Cauley, Stephen; Maier, Andreas (ISMRM & SMRT Annual Conference & Exhibition, 2021)

Pseudo-Square Tomosynthesis Trajectory for Interventional Lung Procedures - Rajput, Junaid Rasool; Preuhs, Alexander; Lauritsch, Guenter; Maier, Andreas (2021 IEEE Nuclear Science Symposium and Medical Imaging Conference Record, NSS/MIC 2021 and 28th International Symposium on Room-Temperature Semiconductor Detectors, RTSD 2022, 2021)
Digital tomosynthesis (DTS) is a 3-D imaging technique with limited depth resolution and can be used to guide a catheter during interventional lung procedures. DTS uses projection data acquired over a limited angular range to create tomographic slices with high in-plane resolution. This limited angular acquisition results in out-of-plane artifacts in the reconstructed volume. This work focused on improving the image quality of the tomosynthesis volume for lung imaging by selecting an acquisition trajectory that increases depth resolution, provides a 2-D blur response, and simplifies analytical reconstruction. We proposed a new pseudo-square acquisition geometry for DTS and performed the quantitative and qualitative comparison with arc (pseudo-linear) and circle acquisition geometries. We reconstructed DTS volumes for various angular ranges and numbers of projections for arc, circle, and pseudo-square trajectories using filtered back-projection and compared them with reference computed tomography (CT) scan using Pearson correlation coefficient (PCC). Based on simulations, we showed that pseudo-square acquisition resulted in better image quality than inbounded arc and inbounded circle geometries. It provided on average 5.5% and 26% higher PCC with reference CT than circle and arc DTS, respectively.
CRIS-Team Scopus Importer:2023-03-07
Keywords: blur response; filtered back-projection; lung nodule; tomosynthesis

Pushing the limits for medical image reconstruction on recent standard multicore processors - Eitzinger, Jan; Hager, Georg; Hofmann, Hannes; Hornegger, Joachim; Wellein, Gerhard (International Journal of High Performance Computing Applications, 2013)
Volume reconstruction by backprojection is the computational bottleneck in many interventional clinical computed tomography (CT) applications. Today vendors in this field replace special purpose hardware accelerators with standard hardware such as multicore chips and GPGPUs. Medical imaging algorithms are on the verge of employing high-performance computing (HPC) technology, and are therefore an interesting new candidate for optimization. This paper presents low-level optimizations for the backprojection algorithm, guided by a thorough performance analysis on four generations of Intel multicore processors (Harpertown, Westmere, Westmere EX, and Sandy Bridge). We choose the RabbitCT benchmark, a standardized testcase well supported in industry, to ensure transparent and comparable results. Our aim is to provide not only the fastest possible implementation but also compare with performance models and hardware counter data in order to fully understand the results. We separate the influence of algorithmic optimizations, parallelization, SIMD vectorization, and microarchitectural issues and pinpoint problems with current SIMD instruction set extensions on standard CPUs (SSE, AVX). The use of assembly language is mandatory for best performance. Finally, we compare our results to the best GPGPU implementations available for this open competition benchmark. © The Author(s) 2012.

Putting 'p' in RabbitCT - Fast CT Reconstruction Using a Standardized Benchmark - Hofmann, Hannes; Keck, Benjamin; Rohkohl, Christopher; Hornegger, Joachim (Mitteilungen - Gesellschaft für Informatik e.V., Parallel-Algorithmen und Rechnerstrukturen, 2009)

QMOS - A Robust Visualization Method for Speaker Dependencies with Different Microphones - Maier, Andreas; Schuster, Maria; Eysholdt, Ulrich; Haderlein, Tino; Cincarek, Tobias; Steidl, Stefan; Batliner, Anton; Wenhardt, Stefan; Nöth, Elmar (Journal of Pattern Recognition Research, 2009)

Quantifying and Leveraging Classification Uncertainty for Chest Radiograph Assessment - Ghesu, Florin-Cristian; Georgescu, Bogdan; Gibson, Eli; Gündel, Sebastian; Kalra, Mannudeep; Singh, Ramandeep; Digumarthy, Subba; Grbic, Sasa; Comaniciu, Dorin (Medical Image Computing and Computer Assisted Intervention – MICCAI 2019, 2019)
The interpretation of chest radiographs is an essential task for the detection of thoracic diseases and abnormalities. However, it is a challenging problem with high inter-rater variability and inherent ambiguity due to inconclusive evidence in the data, limited data quality or subjective definitions of disease appearance. Current deep learning solutions for chest radiograph abnormality classification are typically limited to providing probabilistic predictions, relying on the capacity of learning models to adapt to the high degree of label noise and become robust to the enumerated causal factors. In practice, however, this leads to overconfident systems with poor generalization on unseen data. To account for this, we propose an automatic system that learns not only the probabilistic estimate on the presence of an abnormality, but also an explicit uncertainty measure which captures the confidence of the system in the predicted output. We argue that explicitly learning the classification uncertainty as an orthogonal measure to the predicted output, is essential to account for the inherent variability characteristic of this data. Experiments were conducted on two datasets of chest radiographs of over 85,000 patients. Sample rejection based on the predicted uncertainty can significantly improve the ROC-AUC, e.g., by 8% to 0.91 with an expected rejection rate of under 25%. Eliminating training samples using uncertainty-driven bootstrapping, enables a significant increase in robustness and accuracy. In addition, we present a multi-reader study showing that the predictive uncertainty is indicative of reader errors.<br

Quantitative bias for SPECT imaging in kidneys due to respiratory motion - a simulation study - Sanders, James Chester; Kuwert, Torsten; Vija, A. Hans; Maier, Andreas; Ritt, Philipp (Kongressausgabe 56. Jahrestagung der Deutschen Gesellschaft für Nuklearmedizin, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.quanti_2

Quantitative Bias from Respiratory Motion and Finite System Resolution for SPECT Imaging of Kidneys A Simulation Study - Sanders, James Chester; Kuwert, Torsten; Vija, A. Hans; Maier, Andreas; Ritt, Philipp (Journal of Nuclear Medicine (Supplement 1), 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.quanti_6

Quantitative Biomarkers for Abdominal Magnetic Resonance Imaging - Schneider, Manuel (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2022)
The global prevalence of various metabolic pathologies, such as the metabolic syndrome, obesity, diabetes or nonalcoholic fatty liver disease (NAFLD) has increased steadily in recent decades, and takes on the proportions of a worldwide epidemic. It is therefore important to understand background and pathogenesis of these disorders to reverse this trend. In the last years, research in Magnetic Resonance Imaging (MRI) focused on the extraction of quantitative biomarkers from qualitative MR image data, which allows to quantitatively estimate relaxation properties or signal ratios in the form of parameter maps. Quantitative MRI (qMRI) techniques are accompanied by the promise of earlier disease detection and better grading and staging of diseases. Since MRI is a non-invasive and non-ionizing modality, it allows large-scale research studies and clinical assessment. Widespread clinical adoption of qMRI for the investigation of the described disorders is still limited. Conventional methods suffer from lengthy scan times and motion sensitivity, since a number of images have to be acquired and since often Cartesian sequences are applied. In the abdomen this is challenged by respiration, which is why data is typically acquired during breath-holding. Moreover, currently applied biomarkers are limited in terms of their significance for the prediction of disease states. This work includes techniques for the MRI-based estimation of a new biomarker called triglyceride saturation state, which potentially gives more insights into the pathogenesis of metabolic abnormalities or the risk of breast cancer development. To this end, two methods are proposed that calculate 3-D parameter maps of the relative fractions of saturated, mono-unsaturated and poly-unsaturated fatty acids with regard to the total fat content. Both techniques efficiently sample Cartesian multi-echo gradient-echo (GRE) data using bipolar readout gradients and apply low-rank denoising before parameter estimation. Phase errors are either addressed analytically using an eddy-current phase parameter, or by applying novel echo-dependent phase maps. In vitro, the developed methods yielded accurate and reproducible results across different protocols. In vivo, applicability was assessed using measurements in healthy volunteers and patients in the clinical sites abdomen and breast. Moreover, two methods that allow proton-density fat fraction (PDFF) and R2* quantification during free-breathing are proposed. A motion-robust radial stack-of-stars sequence is applied, and iteratively reconstructed using a model-based technique that combines Parallel Imaging (PI) and Compressed Sensing (CS). The developed approaches are capable of estimating self-gated respiratory motion-averaged and motion-resolved parameter maps from undersampled as well as fully-sampled data. Compared to a conventional state-of-the art technique, the motion-averaged reconstruction achieved accurate PDFF values, and the motion-resolved reconstruction accurate PDFF and R2* results in n = 14 patients enrolled in a hepatobiliary research protocol. For patients unable to suspend respiration, the developed methods are promising alternatives to conventional techniques, which typically apply motion-sensitive Cartesian sequences and sample data during breath-holding. In summary, in this work relevant contributions are made to the fields of fat quantification, fatty acid composition calculation and R2* estimation. The proposed techniques address currently existing limitations in the field of qMRI for imaging various disorders in the abdomen.<br
Keywords: Fat and R2* Quantification; Magnetic Resonance Imaging; Quantitative MRI; Triglyceride Saturation State

Quantitative evaluation of denoising algorithms without noise-free ground-truth data - Pfaff, Laura; Wagner, Fabian; Hoßbach, Julian; Preuhs, Elisabeth; Nickel, Marcel Dominik; Wuerfl, Tobias; Maier, Andreas (ISMRM, 2023)

QuaSI: Quantile Sparse Image Prior for Spatio-Temporal Denoising of Retinal OCT Data - Schirrmacher, Franziska; Köhler, Thomas; Husvogt, Lennart; Fujimoto, James G.; Hornegger, Joachim; Maier, Andreas (Medical Image Computing and Computer-Assisted Intervention - MICCAI 2017, Proceedings, Part II, 2017)
Optical coherence tomography (OCT) enables high-resolution and non-invasive 3D imaging of the human retina but is inherently impaired by speckle noise. This paper introduces a spatio-temporal denoising algorithm for OCT data on a B-scan level using a novel quantile sparse image (QuaSI) prior. To remove speckle noise while preserving image structures of diagnostic relevance, we implement our QuaSI prior via median filter regularization coupled with a Huber data fidelity model in a variational approach. For efficient energy minimization, we develop an alternating direction method of multipliers (ADMM) scheme using a linearization of median filtering. Our spatio-temporal method can handle both, denoising of single B-scans and temporally consecutive B-scans, to gain volumetric OCT data with enhanced signal-to-noise ratio. Our algorithm based on 4 B-scans only achieved comparable performance to averaging 13 B-scans and outperformed other current denoising methods.
UnivIS-Import:2018-09-11:Pub.2017.tech.IMMD.IMMD5.quasiq_2

Radial Basis Function Interpolation for Rapid Interactive Segmentation of 3-D Medical Images - Mirshahzadeh, Negar; Kurzendorfer, Tanja; Fischer, Peter; Pohl, Thomas; Brost, Alexander; Steidl, Stefan; Maier, Andreas (MIUA 2017: Medical Image Understanding and Analysis, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.radial
Keywords: 3-D interpolation; HRBF; Segmentation; Smart brush

Random Forest Based Left Ventricle Segmentation in LGE-MRI - Kurzendorfer, Tanja; Forman, Christoph; Brost, Alexander; Maier, Andreas (Functional Imaging and Modelling of the Heart, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.random

Range Imaging for Motion Compensation in C-Arm Cone-Beam CT of Knees under Weight-Bearing Conditions - Bier, Bastian; Ravikumar, Nishant; Unberath, Mathias; Levenstone, Marc; Gold, Garry; Fahrig, Rebecca; Maier, Andreas (Journal of Imaging, 2018)
UnivIS-Import:2018-09-11:Pub.2018.tech.IMMD.IMMD5.rangei

Rapid Interactive and Intuitive Segmentation of 3D Medical Images Using Radial Basis Function Interpolation - Kurzendorfer, Tanja; Fischer, Peter; Mirshahzadeh, Negar; Pohl, Thomas; Brost, Alexander; Steidl, Stefan; Maier, Andreas (Journal of Imaging, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.rapidi

Rapid Robotic X-ray Computed Tomography of Large Assemblies in Automotive Production - Krumm, Michael; Sauerwein, Christoph; Hämmerle, Volker; Heile, Stephan; Schön, Tobias; Jung, Alexander; Sindel, Manfred (2018)

Realistic Image Synthesis of Imperfect Specimens using Generative Networks - Neufeld, Deniz; Würfl, Tobias; Gruber, Roland; Schön, Tobias; Maier, Andreas (Proceedings of the 9th Conference on Industrial Computed Tomography (iCT 2019), 2019)
UnivIS-Import:2019-08-15:Pub.2019.tech.IMMD.IMMD5.realis

Real-Time-Capable GPU-Framework for Depth-Aware Rigid 2D/3D Registration - Utzschneider, Matthias; Wang, Jian; Schaffert, Roman; Borsdorf, Anja; Maier, Andreas (Bildverarbeitung für die Medizin 2017, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.realti

Real-Time Range Imaging in Health Care: A Survey - Bauer, Sebastian; Seitel, Alexander; Hofmann, Hannes; Blum, Tobias; Wasza, Jakob; Balda, Michael; Meinzer, Hans-Peter; Navab, Nassir; Hornegger, Joachim; Maier-Hein, Lena (Time-of-Flight and Depth Imaging. Sensors, Algorithms, and Applications, 2013)
The recent availability of dynamic, dense, and low-cost range imaging has gained widespread interest in health care. It opens up new opportunities and has an increasing impact on both research and commercial activities. This chapter presents a state-of-the-art survey on the integration of modern range imaging sensors into medical applications. The scope is to identify promising applications and methods, and to provide an overview of recent developments in this rapidly evolving domain. The survey covers a broad range of topics, including guidance in computer-assisted interventions, operation room monitoring and workflow analysis, touch-less interaction and on-patient visualization, as well as prevention and support in elderly care and rehabilitation. We put emphasis on dynamic and interactive tasks where real-time and dense 3-D imaging forms the key aspect. While considering different range imaging modalities that fulfill these requirements, we particularly investigate the impact of Time-of-Flight imaging in this domain. Eventually, we discuss practical demands and limitations, and open research issues and challenges that are of fundamental importance for the progression of the field. © Springer-Verlag 2013.
UnivIS-Import:2015-04-20:Pub.2013.tech.IMMD.IMMD5.realti_6

Real-Time Respiratory Motion Analysis Using 4-D Shape Priors - Wasza, Jakob; Fischer, Peter; Leutheuser, Heike; Oefner, Tobias; Bert, Christoph; Maier, Andreas; Hornegger, Joachim (IEEE Transactions on Biomedical Engineering, 2016)
Respiratory motion analysis based on range imaging (RI) has emerged as a popular means of generating respiration surrogates to guide motion management strategies in computer-assisted interventions. However, existing approaches employ heuristics, require substantial manual interaction, or yield highly redundant information. In this paper, we propose a framework that uses preprocedurally obtained 4-D shape priors from patient-specific breathing patterns to drive intraprocedural RI-based real-time respiratory motion analysis. As the first contribution, we present a shape motion model enabling an unsupervised decomposition of respiration induced high-dimensional body surface displacement fields into a low-dimensional representation encoding thoracic and abdominal breathing. Second, we propose a method designed for GPU architectures to quickly and robustly align our models to high-coverage multiview RI body surface data. With our fully automatic method, we obtain respiration surrogates yielding a Pearson correlation coefficient (PCC) of 0.98 with conventional surrogates based on manually selected regions on RI body surface data. Compared to impedance pneumography as a respiration signal that measures the change of lung volume, we obtain a PCC of 0.96. Using off-the-shelf hardware, our framework enables high temporal resolution respiration analysis at 50 Hz.
Keywords: Graphics processing unit (GPU); radiation therapy; range imaging (RI); respiratory motion

Recognizing Characters in Art History Using Deep Learning - Madhu, Prathmesh; Kosti, Ronak Vijaypal; Mührenberg, Lara; Bell, Peter; Maier, Andreas; Christlein, Vincent (Recognizing Characters in Art History Using Deep Learning, 2019)
In the field of Art History, images of artworks and their contexts are core to understanding the underlying semantic information. However, the highly complex and sophisticated representation of these artworks makes it difficult, even for the experts, to analyze the scene. From the computer vision perspective, the task of analyzing such artworks can be divided into sub-problems by taking a bottom-up approach. In this paper, we focus on the problem of recognizing the characters in Art History. From the iconography of Annunciation of the Lord (Figure 1), we consider the representation of the main protagonists, Mary and Gabriel, across different artworks and styles. We investigate and present the findings of training a character classifier on features extracted from their face images. The limitations of this method, and the inherent ambiguity in the representation of Gabriel, motivated us to consider their bodies (a bigger context) to analyze in order to recognize the characters. Convolutional Neural Networks (CNN) trained on the bodies of Mary and Gabriel are able to learn person related features and ultimately improve the performance of character recognition. We introduce a new technique that generates more data with similar styles, effectively creating data in the similar domain. We present experiments and analysis on three different models and show that the model trained on domain related data gives the best performance for recognizing character. Additionally, we analyze the localized image regions for the network predictions.<br
Keywords: Recognizing Characters; Machine Learning; Convolutional Neural Networks

Reconstruction from truncated projections in cone-beam CT using an efficient 1D filtering - Xia, Yan; Maier, Andreas; Hofmann, Hannes; Dennerlein, Frank; Müller, Kerstin; Hornegger, Joachim (Medical Imaging 2013: Physics of Medical Imaging, 2013)
In X-ray imaging, a reduction of the field of view (FOV) is proportional to a reduction in radiation dose. The resulting truncation, however, is incompatible with conventional tomographic reconstruction algorithms. This problem has been studied extensively. Very recently, a novel method for region of interest (ROI) reconstruction from truncated projections with neither the use of prior knowledge nor explicit extrapolation has been published, named Approximated Truncation Robust Algorithm for Computed Tomography (ATRACT). It is based on a decomposition of the standard ramp filter into a 2D Laplace filtering (local operation) and a 2D Radon-based filtering step (non-local operation). The 2D Radon-based filtering that involves many interpolations complicates the filtering procedure in ATRACT, which essentially limits its practicality. In this paper, an optimization for this shortcoming is presented. That is to apply ATRACT in one dimension, which implies that we decompose the standard ramp filter into the 1D Laplace filter and a 1D convolutionbased filter. The convolution kernel was determined numerically by computing the 1D impulse response of the standard ramp filtering coupled with the second order anti-derivative operation. The proposed algorithm was evaluated by using a reconstruction benchmark test, a real phantom and a clinical data set in terms of spatial resolution, computational efficiency as well as robustness of correction quality. The evaluation outcomes were encouraging. The proposed algorithm showed improvement in computational performance with respect to the 2D ATRACT algorithm and furthermore maintained reconstructions of high accuracy in presence of data truncation. © 2013 SPIE.

Reconstruction of scalar and vectorial components in X-ray dark-field tomography - Bayer, Florian; Hu, Shiyang; Maier, Andreas; Weber, Thomas; Anton, Gisela; Michel, Thilo; Riess, Christian (Proceedings of the National Academy of Sciences of the United States of America, 2014)
Grating-based X-ray dark-field imaging is a novel technique for obtaining image contrast for object structures at size scales below setup resolution. Such an approach appears particularly beneficial for medical imaging and nondestructive testing. It has already been shown that the dark-field signal depends on the direction of observation. However, up to now, algorithms for fully recovering the orientation dependence in a tomographic volume are still unexplored. In this publication, we propose a reconstruction method for grating-based X-ray dark-field tomography, which models the orientation- dependent signal as an additional observable from a standard tomographic scan. In detail, we extend the tomographic volume to a tensorial set of voxel data, containing the local orientation and contributions to dark-field scattering. In our experiments, we present the first results of several test specimens exhibiting a heterogeneous composition in microstructure, which demonstrates the diagnostic potential of the metho
UnivIS-Import:2015-04-14:Pub.2014.nat.dphy.PI.LEPDF.recons
Keywords: Anisotropic scattering; Grating interferometer; Microstructure orientation; X-ray phase contrast

Reconstruction of Voxels with Position- and Angle-Dependent Weightings - Felsner, Lina; Würfl, Tobias; Syben-Leisner, Christopher; Roser, Philipp; Preuhs, Alexander; Maier, Andreas; Riess, Christian (The 6th International Conference on Image Formation in X-Ray Computed Tomography, 2020)

Recurrence Plot Spacial Pyramid Pooling Network for Appliance Identification in Non-Intrusive Load Monitoring - Wenninger, Marc; Bayerl, Sebastian P.; Maier, Andreas; Schmidt, Jochen (20TH IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA 2021), 2021)
CRIS-Team WoS Importer:2022-05-27

Recursive Convolutional Neural Networks for Epigenomics - Symeonidi, Aikaterini; Nicolaou, Anguelos; Johannes, Frank; Christlein, Vincent (25th International Conference on Pattern Recognition (ICPR), 2021)
Deep learning methods have proved to be powerful classification tools in the fields of structural and functional genomics. In this paper, we introduce Recursive Convolutional Neural Networks (RCNN) for the analysis of epigenomic data. We focus on the task of predicting gene expression from the intensity of histone modifications. The proposed RCNN architecture can be applied to data of an arbitrary size, and has a single meta-parameter that quantifies the models capacity, thus making it flexible for experimenting. The proposed architecture outperforms state-of-the-art systems, while having several orders of magnitude fewer parameters.<br
Keywords: Deep learning; Genomics; Tools; Data models; Pattern recognition; Convolutional neural networks; Gene expression

Reducing the risk of hallucinations with interpretable deep learning models for low-dose CT denoising: comparative performance analysis - Patwari, Mayank; Gutjahr, Ralf; Marcus, Roy; Thali, Yannick; Calvarons, Adria F.; Raupach, Rainer; Maier, Andreas (Physics in Medicine and Biology, 2023)
Objective.Reducing CT radiation dose is an often proposed measure to enhance patient safety, which, however results in increased image noise, translating into degradation of clinical image quality. Several deep learning methods have been proposed for low-dose CT (LDCT) denoising. The high risks posed by possible hallucinations in clinical images necessitate methods which aid the interpretation of deep learning networks. In this study, we aim to use qualitative reader studies and quantitative radiomics studies to assess the perceived quality, signal preservation and statistical feature preservation of LDCT volumes denoised by deep learning. We aim to compare interpretable deep learning methods with classical deep neural networks in clinical denoising performance.Approach.We conducted an image quality analysis study to assess the image quality of the denoised volumes based on four criteria to assess the perceived image quality. We subsequently conduct a lesion detection/segmentation study to assess the impact of denoising on signal detectability. Finally, a radiomic analysis study was performed to observe the quantitative and statistical similarity of the denoised images to standard dose CT (SDCT) images.Main results.The use of specific deep learning based algorithms generate denoised volumes which are qualitatively inferior to SDCT volumes(p< 0.05). Contrary to previous literature, denoising the volumes did not reduce the accuracy of the segmentation (p> 0.05). The denoised volumes, in most cases, generated radiomics features which were statistically similar to those generated from SDCT volumes (p> 0.05).Significance.Our results show that the denoised volumes have a lower perceived quality than SDCT volumes. Noise and denoising do not significantly affect detectability of the abdominal lesions. Denoised volumes also contain statistically identical features to SDCT volumes.
CRIS-Team Scopus Importer:2023-10-20
Keywords: artificial intelligence; computed tomography; deep learning; image reconstruction; inverse problems; noise

Reduction of Metal Artifacts using a New Segmentation Approach - Kuhnert, Nadine; Barth, Karl; Maass, Nicole; Maier, Andreas (Bildverarbeitung für die Medizin, 2016)
Metal artifact reduction (MAR) is crucial for the diagnostic value, as metal artifacts tremendously impair the image quality of a CT scan. Wang et al. [1] presented an iterative MAR approach, whereas Kachelriess et al. [2] proposed a filtering method to reduce metal artifacts. However, both techniques are very time-consuming. Most MAR methods contain a metal object segmentation step and the resulting image quality highly depends on the validity of the segmentation [3]. However, segmenting the metal parts correctly poses a non-trivial problem due to the metal artifacts. We present a novel approach of an automatic, object independent segmentation which starts with the state-of-the-art segmentation. This is improved by applying graph cut [4] onto every projection. We extend the graph cut idea by more information and apply knowledge about the distance, a classification probability and a bias to the edges as well as a similarity measure of pixels to their direct neighbors. By additionally considering global consistency, we receive a more precise segmentation result. For the evaluation, our new segmentation approach was combined with the frequency split MAR (FSMAR) [5]. The resulting CT images yielded higher image quality compared with the standard threshold-based FSMAR.
Keywords: Segmentation Result; Metal Artifact; Metal Object; Segmentation Approach; Initial Segmentation

Reduction of metal artifacts using a new segmentation approach: Extension of graph cuts for a more precise segmentation used in metal artifact reduction - Kuhnert, Nadine; Maass, Nicole; Barth, Karl; Maier, Andreas (Informatik aktuell, 2017)
Metal artifact reduction (MAR) is crucial for the diagnostic value, as metal artifacts tremendously impair the image quality of a CT scan. Existing techniques are time-consuming. Most MAR methods contain a metal object segmentation step and the resulting image quality highly depends on the validity of the segmentation. However, segmenting the metal parts correctly still poses a non-trivial problem. We present a novel approach of an automatic, object independent segmentation which starts with the state-of-the-art segmentation. This is improved by applying graph cut onto every projection. We extend the graph cut idea by more information and apply knowledge about the distance, a classification probability and a bias to the edges as well as a similarity measure of pixels to their direct neighbors. By additionally considering global consistency, we receive a more precise segmentation result. For the evaluation, our new segmentation approach was combined with the frequency split MAR (FSMAR). The resulting CT images yielded higher image quality compared with the standard threshold-based FSMAR.
CRIS-Team Scopus Importer:2022-06-05

Reduktion von Metallartefakten durch multipositionale Datenfusion in der industriellen Röntgen-Computertomographie - Herl, Gabriel; Hiller, Jochen; Kasperl, Stefan; Maier, Andreas (Technisches Messen, 2019)
Zusammenfassung Metallartefakte stellen eine große Herausforderung für das Messen mit Röntgen-Computertomographie dar. Dieser Beitrag stellt die Methode der multipositionalen Datenfusion zur Reduktion von Metallartefakten vor. Dazu werden mehrere Scans desselben Objekts bei unterschiedlicher Objekt-positionierung durchgeführt, aufeinander registriert und zur Fusion gemeinsam unter Betrachtung eines lokalen Gütemaßes rekonstruiert. In praxisnahen Experimenten wird der Mehrwert der Methode gezeigt. Insbesondere wird dargestellt, wie mit wenig Aufwand und ohne Vorwissen Kunststoffstrukturen trotz starker Metallartefakte sichtbar gemacht werden können, womit das Verfahren ein Alleinstellungsmerkmal gegenüber den existierenden Metallartefaktreduktionsverfahren aufweist. Abstract Metal artifacts represent a major challenge for measuring with X-ray computed tomography. This paper presents a method for multipositional data fusion for the reduction of metal artifacts. For this purpose, several scans of the same object are performed with different object positioning, registered on each other and then reconstructed together using a local quality measure. The added value of the method is demonstrated in practical experiments. In particular, it is shown how plastic structures can be made visible despite strong metal artifacts with little effort and without prior knowledge, giving the method a unique selling point compared to existing metal artefact reduction methods.
Keywords: X-ray computed tomography; CT; metal artifact reduction; multipositional data fusion

Redundancies in X-ray images due to the epipolar geometry for transmission imaging - Aichert, André; Maass, Nicole; Deuerling-Zheng, Yu; Berger, Martin; Manhart, Michael; Hornegger, Joachim; Maier, Andreas; Dörfler, Arnd (Proceedings of the third international conference on image formation in x-ray computed tomography, 2014)
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.redund_1
Keywords: GRK-1773

Reference Algorithms for the Mitosis Domain Generalization (MIDOG) 2022 Challenge - Ammeling, Jonas; Wilm, Frauke; Ganz, Jonathan; Breininger, Katharina; Aubreville, Marc (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2023)
Robust mitosis detection on images from different tumor types, pathology labs, and species is a challenging task that was addressed in the MICCAI Mitosis Domain Generalization (MIDOG) 2022 challenge. In this work, we describe three reference algorithms that were provided as a baseline for the challenge: A Mask-RCNN-based instance segmentation model trained on the MIDOG 2022 dataset, and two different versions of the domain-adversarial RetinaNet which already served as the baseline for MIDOG 2021 challenge, one trained on the MIDOG 2022 dataset and the other trained only on human breast carcinoma from MIDOG 2021. The domain-adversarial RetinaNet trained on the MIDOG 2022 dataset had the highest F 1 score of 0.7135 on the final test set. When trained on breast carcinoma only, the same network had a much lower F 1 score of 0.4719, indicating a significant domain shift between mitotic figure and tissue representation in different tumor types.
CRIS-Team Scopus Importer:2023-10-20

Reference database of total retinal vessel surface area derived from volume-rendered optical coherence tomography angiography - Maloca, Peter M.; Feu-Basilio, Silvia; Schottenhamml, Julia; Valmaggia, Philippe; Scholl, Hendrik P.N.; Rosinés-Fonoll, Josep; Marin-Martinez, Sara; Inglin, Nadja; Reich, Michael; Lange, Clemens; Egan, Catherine; Zweifel, Sandrine; et al. (Scientific Reports, 2022)
Optical coherence tomography angiography (OCTA) enables three-dimensional, high-resolution, depth-resolved flow to be distinguished from non-vessel tissue signals in the retina. Thus, it enables the quantification of the 3D surface area of the retinal vessel signal. Despite the widespread use of OCTA, no representative spatially rendered reference vessel surface area data are published. In this study, the OCTA vessel surface areas in 203 eyes of 107 healthy participants were measured in the 3D domain. A Generalized Linear Model (GLM) model analysis was performed to investigate the effects of sex, age, spherical equivalent, axial length, and visual acuity on the OCTA vessel surface area. The mean overall vessel surface area was 54.53 mm2 (range from 27.03 to 88.7 mm2). OCTA vessel surface area was slightly negatively correlated with age. However, the GLM model analysis identified axial length as having the strongest effect on OCTA vessel surface area. No significant correlations were found for sex or between left and right eyes. This is the first study to characterize three-dimensional vascular parameters in a population based on OCTA with respect to the vessel surface area.
CRIS-Team Scopus Importer:2022-03-18

Reference-free Three-Dimensional Reconstruction of Digitized Histological Slice Sequences - Gaffling, Simone (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2021)
Although first 3-D reconstructions of minute structures were made over 130 years ago, the need to understand the shape and morphology of various specimens at the microscopic level has increased significantly in the last decade. Since volumetric imaging techniques like computed tomography or magnetic resonance imaging are still limited regarding spatial resolution and availability, an interest in the digital reconstruction of histological tissue preparations prepared and stained according to the scientific question has evolved. One fundamental technique in 3D histology reconstruction is the use of image registration for alignment of slice images and reversal of slice deformations. Here, the individual registration components are often chosen to best fit the data at hand. This thesis uses a rigid transformation model for simple alignment and a non-rigid, non-parametric image transform for image unwarping. The core principle of the unwarping strategy is the iterative Gauss-Seidel method, which is capable of selectively eliminating higher frequency errors from given functions consisting of superimposed signals of different wavelengths. Evaluation strategies like the Sum of Squared Distances, Target Registration Error and Graylevel Cooccurrence Matrices are ways to assess the quality of match between slices and the coherence of reconstructed histology volumes. The histological preparation procedure steps like resection, embedding, cutting and staining significantly destroy the tissue, and the digitization step leads to disambiguities and information loss regarding the shape of structures. The image data sets are therefore inherently afflicted with a large number of artifacts like intensity variances, foldings, deformations or missing parts. The preprocessing and reconstruction pipeline starts with the gray value conversion of color images to reduce the computational effort. As one of the two main contributions of the work a novel intensity standardization for histological image sequences is proposed. Here, percentile values of individual slice histograms are determined, and corresponding percentile values treated as list of function values. The Gauss-Seidel method is used to eliminate the higher frequency intensity variances, but preserve the lower frequency differences stemming from changing tissue content. For an entire slice data set, a mean intensity correction of 0.45 and mean standard deviation of correction of only 5.03 – half of the correction other methods achieved – demonstrates a moderate adaption of the original intensity values, while qualitative results show good contrast and smooth appearance. After optional replacement of defective or missing slice images and possibly manual sorting of the image data set, a first simple 3D reconstruction is achieved by rigid image registration and stacking. The remaining nonlinear slice deformations prevent real coherence of the anatomy. The second main contribution of the work is the adaptation of the Gauss-Seidel method for image sequences. While the non-rigid image registration process provides a possibility to emulate the slice deformations and thus to reverse them, the Gauss- Seidel method determines how these deformations are eliminated by an iterative update of the individual images, taking into account their local neighborhood. The method is suitable for large datasets, since it does not require the entire dataset in memory. Experiments show that the method converges quickly, and experiments with synthetic data with known deformations suggest that the true deformation of the slices is effectively reversed, with the Mean Target Registration Error being below 1 pixel offset to the original CT data set after 5 iterations. Visually, the results are convincing and provide instructive insights into microscopic morphology. Further improvements of the proposed reconstruction pipeline include a gray level conversion based on stain protocol or structure-of-interest, better defect slice replacement methods, or automated methods to restore the correct slice sequence. The intensity matching method could be adapted based on the tissue content. For large data sets, the Gauss-Seidel unwarping method should be modified to a multilevel approach. Possible applications include better anatomical teaching, improvement of in-vivo 3D imaging protocols or digital pathology.<br
Keywords: Gauss-Seidel Method for Deformation Reversal; Histology Reconstruction; Histology Sequences; Reconstruction

Region of Interest Reconstruction from Dose-minimized Super Short Scan Data - Xia, Yan; Maier, Andreas; Berger, Martin; Hornegger, Joachim (Bildverarbeitung für die Medizin 2014, 2014)
In this paper, we investigate an ROI image reconstruction in super short scan. The redundant rays in such scan are further shielded by using dynamic collimation. The acquired data coming with an extremely minimized radiation dose are sufficient for image reconstruction. For compensating resulting truncation, we apply a recently proposed algorithm – Approximated Truncation Robust Algorithm for Computed Tomography (ATRACT). The evaluation with two clinical datasets demonstrates that high image quality is achieved using this super short scan, with more dose reduction compared to a standard short scan.
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.region_7
Keywords: GRK-1773

Region-Of-Interest Reconstruction on medical C-arms with the ATRACT Algorithm - Dennerlein, Frank; Maier, Andreas (Proc. SPIE 8313, 2012)

Registration of Atrium Models to C-arm X-ray Images Based on Devices Inside the Coronary Sinus and the Esophagus - Hoffmann, Matthias; Strobel, Norbert; Maier, Andreas (Bildverarbeitung für die Medizin 2016, 2016)
UnivIS-Import:2016-06-01:Pub.2016.tech.IMMD.IMMD5.regist

Registration of vascular structures using a hybrid mixture model - Bayer, Siming; Zhai, Zhiwei; Strumia, Maddalena; Tong, Xiaoguang; Gao, Ying; Staring, Marius; Stoel, Berend; Fahrig, Rebecca; Arya, Nabavi; Maier, Andreas; Ravikumar, Nishant (International Journal of Computer Assisted Radiology and Surgery, 2019)
Purpose Morphological changes to anatomy resulting from invasive surgical procedures or pathology, typically alter the surrounding vasculature. This makes it useful as a descriptor for feature-driven image registration in various clinical applications. However, registration of vasculature remains challenging, as vessels often differ in size and shape, and may even miss branches, due to surgical interventions or pathological changes. Furthermore, existing vessel registration methods are typically designed for a specific application. To address this limitation, we propose a generic vessel registration approach useful for a variety of clinical applications, involving different anatomical regions. Methods A probabilistic registration framework based on a hybrid mixture model, with a refinement mechanism to identify missing branches (denoted as HdMM+) during vasculature matching, is introduced. Vascular structures are represented as 6-dimensional hybrid point sets comprising spatial positions and centerline orientations, using Student’s t-distributions to model the former and Watson distributions for the latter. Results The proposed framework is evaluated for intraoperative brain shift compensation, and monitoring changes in pulmonary vasculature resulting from chronic lung disease. Registration accuracy is validated using both synthetic and patient data. Our results demonstrate, HdMM+ is able to reduce more than 85%" role="presentation">85% of the initial error for both applications, and outperforms the state-of-the-art point-based registration methods such as coherent point drift and Student’s t-distribution mixture model, in terms of mean surface distance, modified Hausdorff distance, Dice and Jaccard scores. Conclusion The proposed registration framework models complex vascular structures using a hybrid representation of vessel centerlines, and accommodates intricate variations in vascular morphology. Furthermore, it is generic and flexible in its design, enabling its use in a variety of clinical applications.
Keywords: Non-rigid registration; Point matching; Brain shift; Pulmonary vascular diseases

Relationship between CC Impairment and GA Growth: An Optical Coherence Tomography Angiography Study - Moult, Eric; Alibhai, A. Yasin; Lee, Byungkun; Ploner, Stefan; Maier, Andreas; Duker, Jay S.; Waheed, Nadia K.; Fujimoto, James G. (INVESTIGATIVE OPHTHALMOLOGY & VISUAL SCIENCE, 2019)
CRIS-Team WoS Importer:2019-10-29

Repeatability and Reproducibility of a New Method for Quantifying Triglyceride Saturation Using Bipolar Multi-Echo MRI - Schneider, Manuel; Lugauer, Felix; Janas, Gemini; Nickel, Dominik; Dale, Brian M.; Kiefer, Berthold; Maier, Andreas; Bashir, Mustafa R. (Proceedings of the Joint Annual Meeting ISMRM-ESMRMB, 2018)
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.repeat

Representation Learning for Cloud Classification - Bernecker, David; Riess, Christian; Christlein, Vincent; Angelopoulou, Elli; Hornegger, Joachim (Pattern Recognition, 2013)

Reproducibility in dual energy CT: the impact of a projection domain material decomposition method - Haase, V.; Noo, F.; Stierstorfer, K.; Maier, Andreas; McNitt-Gray, M. (Proceedings of SPIE - The International Society for Optical Engineering, 2022)
Reproducibility of CT numbers represents an ongoing challenge, especially in clinical applications where exams are performed to assess disease progression or response to therapy. Dual energy CT (DECT) offers an opportunity for improved image quantification, but also presents unique issues. This work presents an initial phantom study that investigates reproducibility over time, across different scanners, and with regard to positioning of the phantom. Both an image domain and a projection data domain material decomposition method are used to create mono-energetic images from DECT data. The scanned object is the ACR CT accreditation phantom. Images were evaluated for reproducibility both inside the phantom inserts of module A as well as in regions between the inserts, where image artifacts are frequently visualized. The results demonstrate that artifacts are worse for off-centered positions. They also demonstrate that the data-based material decomposition provides comparable HU numbers within the inserts of interest like the image-based method, but provides substantially less artifacts and less HU variability in regions surrounding the inserts across the different phantom positions.
CRIS-Team Scopus Importer:2022-11-25

Re-Ranking for Writer Identification and Writer Retrieval - Jordan, Simon; Seuret, Mathias; Král, Pavel; Lenc, Ladislav; Martínek, Jiří; Wiermann, Barbara; Schwinger, Tobias; Maier, Andreas; Christlein, Vincent (Document Analysis Systems, 2020)
Automatic writer identification is a common problem in document analysis. State-of-the-art methods typically focus on the feature extraction step with traditional or deep-learning-based techniques. In retrieval problems, re-ranking is a commonly used technique to improve the results. Re-ranking refines an initial ranking result by using the knowledge contained in the ranked result, e. g., by exploiting nearest neighbor relations. To the best of our knowledge, re-ranking has not been used for writer identification/retrieval. A possible reason might be that publicly available benchmark datasets contain only few samples per writer which makes a re-ranking less promising. We show that a re-ranking step based on k-reciprocal nearest neighbor relationships is advantageous for writer identification, even if only a few samples per writer are available. We use these reciprocal relationships in two ways: encode them into new vectors, as originally proposed, or integrate them in terms of query-expansion. We show that both techniques outperform the baseline results in terms of mAP on three writer identification datasets.<br
Keywords: Writer identification; Writer retrieval; Re-ranking

Resolve Intraoperative Brain Shift as Imitation Game - Zhong, Xia; Bayer, Siming; Ravikumar, Nishant; Strobel, Norbert; Birkhold, Annette; Kowarschik, Markus; Fahrig, Rebecca; Maier, Andreas (MICCAI Challenge 2018 for Correction of Brainshift with Intra-Operative Ultrasound (CuRIOUS 2018), 2018)
Soft tissue deformation induced by craniotomy and tissue manipulation (brain shift) limits the use of preoperative image overlay in an image-guided neurosurgery, and therefore reduces the accuracy of the surgery as a consequence. An inexpensive modality to compensate for the brain shift in real-time is Ultrasound (US). The core subject of research in this context is the non-rigid registration of preoperative MR and intraoperative US images. In this work, we propose a learning based approach to address this challenge. Resolving intraoperative brain shift is considered as an imitation game, where the optimal action (displacement) for each landmark on MR is trained with a multi-task network. The result shows a mean target error of 1.21 ± 0.55 mm.<br

Respiratory Deformation Estimation in X-Ray-Guided IMRT Using a Bilinear Model - Geimer, Tobias; Ploner, Stefan; Keall, Paul; Bert, Christoph; Maier, Andreas (Informatik aktuell, 2019)
Driving a respiratory motion model in X-ray guided radiotherapy can be challenging in treatments with continuous rotation such as VMAT, as data-driven respiratory signal extraction suffers from angular effects overlapping with respiratory changes in the projection images. Compared to a linear model trained on static acquisition angles, the bilinear model gains flexibility in terms of handling multiple viewpoints at the cost of accuracy. In this paper, we evaluate both models in the context of serving as the surrogate input to a motion model. Evaluation is performed on the 20 patient 4D CTs in a leave-one-phase-out approach yielding a median accuracy drop of only 0.14mm in the 3D error of estimated vector fields of the bilinear model compared to the linear one.
CRIS-Team Scopus Importer:2019-05-14

Respiratory Motion Compensation for C-arm CT Liver Imaging - Sindel, Aline; Bögel, Marco; Maier, Andreas; Fahrig, Rebecca; Hornegger, Joachim; Dörfler, Arnd (Bildverarbeitung für die Medizin 2015, 2015)
UnivIS-Import:2015-04-17:Pub.2015.tech.IMMD.IMMD5.respir

Respiratory Motion Compensation in Rotational Angiography: Graphical Model-Based Optimization of Auto-Focus Measures - Unberath, Mathias; Taubmann, Oliver; Bier, Bastian; Geimer, Tobias; Hell, Michaela; Achenbach, Stephan; Maier, Andreas (IEEE International Symposium on Biomedical Imaging, Proceedings of the, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.respir_2
Keywords: Angiography; Cardiac imaging; Cone-beam CT; Discrete optimization; Markov random fields

Respiratory Motion Compensation Using Diaphragm Tracking for Cone-Beam C-Arm CT: A Simulation and a Phantom Study - Bögel, Marco; Hofmann, Hannes; Hornegger, Joachim; Fahrig, Rebecca; Britzen, Stefan; Maier, Andreas (International Journal of Biomedical Imaging, 2013)

Respiratory Motion Estimation using a 3D Diaphragm Model - Bögel, Marco; Riess, Christian; Maier, Andreas; Hornegger, Joachim; Fahrig, Rebecca (Bildverarbeitung in der Medizin 2014, 2014)
Long acquisition times of several seconds lead to image artifacts in cardiac C-arm CT. These artifacts are mostly caused by respiratory motion. In order to improve image quality, it is important to accurately estimate the breathing motion that occurred during image acquisition. It has been shown that diaphragm motion is correlated to the respiration-induced motion of the heart. We present a method to estimate an accurate three-dimensional (3D) model of the diaphragm and its compression motion field from a set of C-arm CT projection images acquired during free breathing. First results on the digital XCAT phantom are promising. The method is able to estimate the motion field amplitude exactly. The boundaries of the estimated compression motion field are estimated within 3mm accurac
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.respir_33

Ressource-Efficient Moth Detection for Pest Monitoring with YOLOv5 - Farooq, M. Tallal; Leipert, Martin; Maier, Andreas; Christlein, Vincent (2023 IEEE Symposium Series on Computational Intelligence, 2023)
Moths pose a significant threat to agricultural crops, and identifying them accurately is crucial for effective pest monitoring and crop conservation efforts. However, manually evaluating glue traps is a time-consuming and labor-intensive process, which has led to the development of automated solutions. In this study, we present a deep learning-based automated detection pipeline that can detect moths in images captured by field traps with pheromone-emitting glue pads. To train our model, we collected a comprehensive dataset that includes moths from various environments, such as agricultural plants, homes, and food production facilities. We augmented this dataset and included additional glue pad datasets, enabling the model to detect moths regardless of the species. We base our model on the YOLOv5 algorithm and fine-tune it using transfer learning, which enables us to identify moths in real-time and on embedded hardware. Our evaluation of the algorithm reveals that it achieves an average precision of 98.2 % on a test dataset, which outperforms reference models from previous research. We also assess the model’s ability to handle disturbances such as other insects, varying lighting conditions, and foreign objects. Importantly, our solution maintains a tiny memory footprint and low inference time of 2.3 ms, making it a highly efficient and effective tool for moth detection in the field.<br
Keywords: Pheromone Trap; Pest Management; Insect Detection; Moths

Restoration of Missing Data in Limited Angle Tomography Based on Consistency Conditions - Huang, Yixing; Taubmann, Oliver; Huang, Xiaolin; Hornegger, Joachim; Lauritsch, Günter; Maier, Andreas (The 14th International Meeting on Fully Three‐Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2017)
In limited angle tomography, only a limited angular range of data is acquired and consequently a double wedge-shaped region in the frequency domain representation of the imaged object is missing. Hence, streak artifacts occur. To restore the missing data, we perform a regression and an image fusion in sinogram domain and frequency domain, respectively. We first convert the sinogram restoration problem into a regression problem based on the Helgason-Ludwig consistency conditions. Due to the severe ill-posedness of the problem, regression only partially recovers the correct frequency components, especially lower frequency components, and will introduce erroneous ones, particularly higher frequencies. Bilateral filtering is utilized to retain the most prominent high frequency components and suppress erroneous ones. A fusion of the filtered image and the image reconstructed from the limited angle sinogram is performed afterwards in the frequency domain. The proposed method is evaluated on the Shepp-Logan phantom, for which the root-mean-square error of the reconstructed image decreases from 310 HU to 136 HU.<br
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.restor_2
Keywords: limited angle tomography; data consistency condition; Helgason-Ludwig consistency condition

Restoration of Missing Data in Limited Angle Tomography Based on Helgason-Ludwig Consistency Conditions - Huang, Yixing; Huang, Xiaolin; Taubmann, Oliver; Xia, Yan; Haase, Viktor; Hornegger, Joachim; Lauritsch, Günter; Maier, Andreas (Biomedical Physics and Engineering Express, 2017)
In limited angle tomography, missing data in an insufficient angular scan will cause streak artifacts in the reconstructed images. Correspondingly, in the frequency domain representation of the imaged object, a double wedge-shaped region is missing. In this paper, we perform a regression in sinogram domain and an image fusion in frequency domain to restore the missing data. We first convert the sinogram restoration problem into a regression problem based on the Helgason–Ludwig consistency conditions. Due to its severe ill-posedness, regression only partially recovers the correct frequency components, especially lower frequency components, and will introduce erroneous ones, particularly higher frequencies. Bilateral filtering is utilized to retain the most prominent high frequency components and suppress erroneous ones. Afterwards, a fusion in the frequency domain utilizes the restored frequency components to fill the missing double wedge region. The proposed method is evaluated in a parallel-beam study on both numerical and clinical phantoms. The root-mean-square errors of the reconstructed images decrease from 302 to 78 HU for the noise-free Shepp–Logan phantom, from 355 to 175 HU for the noisy Shepp–Logan phantom, and from 187 to 56 HU for the clinical data. The results show that our method is promising in streak reduction and intensity offset compensation in both noise-free and noisy situations.<br
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.restor
Keywords: limited angle tomography; Helgason-Ludwig consistency conditions; streak artifacts; regression; fusion

Retinal blood flow speed quantification at the capillary level using temporal autocorrelation fitting OCTA [Invited] - Hwang, Yunchan; Won, Jungeun; Yaghy, Antonio; Takahashi, Hiroyuki; Girgis, Jessica M.; Lam, Kenneth; Chen, Siyu; Moult, Eric M.; Ploner, Stefan; Maier, Andreas; Waheed, Nadia K.; Fujimoto, James G. (Biomedical Optics Express, 2023)
Optical coherence tomography angiography (OCTA) can visualize vasculature structures, but provides limited information about blood flow speed. Here, we present a second generation variable interscan time analysis (VISTA) OCTA, which evaluates a quantitative surrogate marker for blood flow speed in vasculature. At the capillary level, spatially compiled OCTA and a simple temporal autocorrelation model, ρ(τ) = exp(-ατ), were used to evaluate a temporal autocorrelation decay constant, α, as the blood flow speed marker. A 600 kHz A-scan rate swept-source OCT prototype instrument provides short interscan time OCTA and fine A-scan spacing acquisition, while maintaining multi mm2 field of views for human retinal imaging. We demonstrate the cardiac pulsatility and assess repeatability of α measured with VISTA. We show different α for different retinal capillary plexuses in healthy eyes and present representative VISTA OCTA in eyes with diabetic retinopathy.
CRIS-Team Scopus Importer:2023-06-30

Rewiring of neuronal networks during synaptic silencing - Wrosch, Jana Katharina; Von Einem, Vicky; Breininger, Katharina; Dahlmanns, Marc; Maier, Andreas; Kornhuber, Johannes; Groemer, Teja Wolfgang (Scientific Reports, 2017)
Analyzing the connectivity of neuronal networks, based on functional brain imaging data, has yielded new insight into brain circuitry, bringing functional and effective networks into the focus of interest for understanding complex neurological and psychiatric disorders. However, the analysis of network changes, based on the activity of individual neurons, is hindered by the lack of suitable meaningful and reproducible methodologies. Here, we used calcium imaging, statistical spike time analysis and a powerful classification model to reconstruct effective networks of primary rat hippocampal neurons in vitro. This method enables the calculation of network parameters, such as propagation probability, path length, and clustering behavior through the measurement of synaptic activity at the single-cell level, thus providing a fuller understanding of how changes at single synapses translate to an entire population of neurons. We demonstrate that our methodology can detect the known effects of drug-induced neuronal inactivity and can be used to investigate the extensive rewiring processes affecting population-wide connectivity patterns after periods of induced neuronal inactivity.
EVALuna2:33558

RGB-D-based Human Detection and Segmentation for Mobile Robot Navigation in Industrial Environments - Kedilioglu, Oguz; Lieret, Markus; Schottenhamml, Julia; Würfl, Tobias; Blank, Andreas; Maier, Andreas; Franke, Jörg (16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021), 2021)

Rigid and Non-Rigid Motion Compensation in Weight-Bearing CBCT of the Knee Using Simulated Inertial Measurements - Maier, Jennifer; Nitschke, Marlies; Choi, Jang Hwan; Gold, Garry; Fahrig, Rebecca; Eskofier, Björn; Maier, Andreas (IEEE Transactions on Biomedical Engineering, 2022)
Objective: Involuntary subject motion is the main source of artifacts in weight-bearing cone-beam CT of the knee. To achieve image quality for clinical diagnosis, the motion needs to be compensated. We propose to use inertial measurement units (IMUs) attached to the leg for motion estimation. Methods: We perform a simulation study using real motion recorded with an optical tracking system. Three IMU-based correction approaches are evaluated, namely rigid motion correction, non-rigid 2D projection deformation and non-rigid 3D dynamic reconstruction. We present an initialization process based on the system geometry. With an IMU noise simulation, we investigate the applicability of the proposed methods in real applications. Results: All proposed IMU-based approaches correct motion at least as good as a state-of-the-art marker-based approach. The structural similarity index and the root mean squared error between motion-free and motion corrected volumes are improved by 24-35% and 78-85%, respectively, compared with the uncorrected case. The noise analysis shows that the noise levels of commercially available IMUs need to be improved by a factor of 105 which is currently only achieved by specialized hardware not robust enough for the application. Conclusion: Our simulation study confirms the feasibility of this novel approach and defines improvements necessary for a real application. Significance: The presented work lays the foundation for IMU-based motion compensation in cone-beam CT of the knee and creates valuable insights for future developments.
CRIS-Team Scopus Importer:2022-05-13
Keywords: CT reconstruction; inertial measurements; motion compensation; noise; non-rigid motion

RinQ Fingerprinting: Recurrence-Informed Quantile Networks for Magnetic Resonance Fingerprinting - Hoppe, Elisabeth; Thamm, Florian; Koerzdoerfer, Gregor; Syben-Leisner, Christopher; Nittka, Mathias; Pfeuffer, Josef; Meyer, Heiko; Maier, Andreas (Bilderverarbeitung für die Medizin Algorithmen - Systeme - Anwendungen, 2020)

RinQ Fingerprinting: Recurrence-Informed Quantile Networks for Magnetic Resonance Fingerprinting - Hoppe, Elisabeth; Thamm, Florian; Körzdörfer, Gregor; Syben-Leisner, Christopher; Schirrmacher, Franziska; Nittka, Mathias; Pfeuffer, Josef; Meyer, Heiko; Maier, Andreas (Proceedings of MICCAI 2019, 2019)
Recently, Magnetic Resonance Fingerprinting (MRF) was proposed as a quantitative imaging technique for the simultaneous acquisition of tissue parameters such as relaxation times T1 and T2. Although the acquisition is highly accelerated, the state-of-the-art reconstruction suffers from long computation times: Template matching methods are used to find the most similar signal to the measured one by comparing it to pre-simulated signals of possible parameter combinations in a discretized dictionary. Deep learning approaches can overcome this limitation, by providing the direct mapping from the measured signal to the underlying parameters by one forward pass through a network. In this work, we propose a Recurrent Neural Network (RNN) architecture in combination with a novel quantile layer. RNNs are well suited for the processing of time-dependent signals and the quantile layer helps to overcome the noisy outliers by considering the spatial neighbors of the signal. We evaluate our approach using in-vivo data from multiple brain slices and several volunteers, running various experiments. We show that the RNN approach with small patches of complex-valued input signals in combination with a quantile layer outperforms other architectures, e.g. previously proposed CNNs for the MRF reconstruction reducing the error in T1 and T2 by more than 80%.<br

Robust blood flow velocity estimation from 3D rotational angiography - Bögel, Marco; Gehrisch, S.; Redel, T.; Rohkohl, C.; Birkhold, A.; Hölter, Philip; Maier, Andreas; Kowarschik, M.; Dörfler, Arnd (Proceedings - International Conference on Image Processing, ICIP, 2018)
Blood flow velocity estimation techniques from 2D fluoroscopy and more recently rotational angiography images represent a topic of wide interest in various clinical research areas. In particular, they can be an important step towards patient-specific flow simulations. Additionally, it can be of diagnostic interest to evaluate volumetric blood flow in stenotic vessel segments; e.g. in a patient's brain. In this work, we present a robust optimization-based approach to estimate mean blood flow velocities from rotational digital subtraction angiography (DSA) images. Our method was extensively evaluated on 70 simulated datasets and 6 clinical datasets with MR phase contrast ground truth data. Our evaluation explores the limitations of image-based velocity estimation; i.e., measurements over short or small vessel segments. Overall, we were able to estimate the mean velocity with average errors as little as 4% for simulation studies, if the vessel segment is sufficiently long, and achieved results within the confines of the MR phase contrast ground truth data for our clinical data, with an average relative error to the centerline measurement of 9.5%±10.5%. The achieved accuracy enables patient-specific hemodynamic simulations and may also be of immediate diagnostic interest.
CRIS-Team Scopus Importer:2022-06-05
Keywords: Angiography; Contrast medium; Flow quantification; Velocity estimation

Robust Camera Pose Estimation for Unordered Road Scene Images in Varying Viewing Conditions - Venator, Moritz; Bruns, Erich; Maier, Andreas (IEEE Transactions on Intelligent Vehicles, 2019)
For continuous performance optimization of camera sensor systems in automated driving, training data from rare corner cases occurring in series production cars are required. In this work, we propose collaborative acquisition of camera images via connected car fleets for synthesis of image sequences from arbitrary road sections which are challenging for machine vision. While allowing a scalable hardware architecture inside the cars, this concept demands to reconstruct the recording locations of the individual images aggregated in the back-end. Varying environmental conditions, dynamic scenes, and small numbers of significant landmarks may hamper camera pose estimation through sparse reconstruction from unordered road scene images. Tackling those problems, we extend a state-of-the-art Structure from Motion pipeline by selecting keypoints based on a semantic image segmentation and removing GPS outliers. We present three challenging image datasets recorded on repetitive test drives under differing environmental conditions for evaluation of our method. The results demonstrate that our optimized pipeline is able to reconstruct the camera viewpoints robustly in the majority of road scenes observed while preserving high image registration rates. Reducing the median deviation from GPS measurements by over 48 % for car fleet images, the method increases the accuracy of camera poses dramatically.<br
Keywords: Structure from Motion; 6-DoF camera pose reconstruction; car fleet images; varying environmental conditions

Robust Classification from Noisy Labels: Integrating Additional Knowledge for Chest Radiography Abnormality Assessment - Gündel, Sebastian; Setio, Arnaud A.A.; Ghesu, Florin-Cristian; Grbic, Sasa; Georgescu, Bogdan; Maier, Andreas; Comaniciu, Dorin (Medical Image Analysis, 2021)
Chest radiography is the most common radiographic examination performed in daily clinical practice for the detection of various heart and lung abnormalities. The large amount of data to be read and reported, with more than 100 studies per day for a single radiologist, poses a challenge in consistently maintaining high interpretation accuracy. The introduction of large-scale public datasets has led to a series of novel systems for automated abnormality classification. However, the labels of these datasets were obtained using natural language processed medical reports, yielding a large degree of label noise that can impact the performance. In this study, we propose novel training strategies that handle label noise from such suboptimal data. Prior label probabilities were measured on a subset of training data re-read by 4 board-certified radiologists and were used during training to increase the robustness of the training model to the label noise. Furthermore, we exploit the high comorbidity of abnormalities observed in chest radiography and incorporate this information to further reduce the impact of label noise. Additionally, anatomical knowledge is incorporated by training the system to predict lung and heart segmentation, as well as spatial knowledge labels. To deal with multiple datasets and images derived from various scanners that apply different post-processing techniques, we introduce a novel image normalization strategy. Experiments were performed on an extensive collection of 297,541 chest radiographs from 86,876 patients, leading to a state-of-the-art performance level for 17 abnormalities from 2 datasets. With an average AUC score of 0.880 across all abnormalities, our proposed training strategies can be used to significantly improve performance scores.
Keywords: label noise; robust loss function; multi-task learning; chest radiography abnormality classification

Robust Heart Valve Modeling and Advanced Computational Decision Support - Voigt, Ingmar (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2023)
Valvular heart disease (VHD) is a major public health problem with a prevalence of 2.5% in the US. Tens of thousands of minimally invasive procedures are performed every year and new therapeutical approaches are continuously developed at a rapid pace. Major clinical decisions and therapies are nowadays guided by peri- and intraoperative imaging with an increasing emphasis on 3D. Given the complexity of pathology and therapies in VHD, there is a strong need for fast, precise and reproducible quantification. To this end, several contributions are proposed in this thesis across various aspects of VHD management. These are structured into two parts: i) Mitral valve (MV) modeling and ii) advanced computational decision support. The first part starts with describing a comprehensive MV model obtained from 3D+t transesophageal echocardiography (TEE). An original methodology combines robust machine learning (ML) techniques and biomechanical constraints to obtain a temporally consistent estimate of model parameters. Extensive experiments on a large data set of 195 3D+t TEE volumes – comprising 3026 3D frames – demonstrate, that this automatic method is highly competitive in terms of speed and accuracy and outperforms purely data-driven methods in terms of robustness. Clinical evaluation in numerous centers around the world shows the applicability of its versatile quantification capabilities to various aspects of disease characterization and therapy planning. Subsequently a more lightweight alternative enables for robust 3D tracking of the MV annulus at high frame rates. Two parallel components based on ML and image-based tracking complement each other in robustness and speed. Experiments with emulated probe motion suggest suitability towards an interventional setting for guiding transcatheter mitral valve repair (TMVR) procedures. The second part proposes computational approaches for advanced decision support throughout the clinical workflow. First, the previously described valve modeling is combined with chamber models into a holistic and detailed model of the left heart. This enables for estimating patient-specific computational hemodynamics by serving as boundary condition for a level-set based computational fluid dynamics (CFD) solver. A validation concept using clinically acquired Doppler measurements is proposed and shows high agreement. Finally, a framework is presented for post-operative modeling of self-expandable stent devices for monitoring in transcatheter aortic valve implantation (TAVI) procedures. The technique is based on deformable simplex meshes, geometrical constraints and ML. Evaluation on postoperative computed tomography (CT) data shows promising accuracy.<br
Keywords: Cardiac Imaging; Heart Valves; Machine Learning

Robust Hough and Spatial-To-Angular Transform Based Rotation Estimation for Orthopedic X-Ray Images - Bachmaier, Magdalena; Rohleder, Maximilian; Swartman, Benedict; Privalov, Maxim; Maier, Andreas; Kunze, Holger (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2023)
Standardized image rotation is essential to improve reading performance in interventional X-ray imaging. To minimize user interaction and streamline the 2D imaging workflow, we present a new automated image rotation method. Image rotation can follow two steps: First, an anatomy specific centerline image is predicted which depicts the desired anatomical axis to be aligned vertically after rotation. In a second step, the necessary rotation angle is calculated from the orientation of the predicted line image. We propose an end-to-end trainable model with the Hough transform (HT) and a differentiable spatial-to-angular transform (DSAT) embedded as known operators. This model allows to robustly regress a rotation angle while maintaining an explainable inner structure and allows to be trained with both a centerline segmentation and angle regression loss. The proposed method is compared to a Hu moments-based method on anterior-posterior X-ray images of spine, knee, and wrist. For the wrist images, the HT based method reduces the mean absolute angular error (MAE) from 9. 28 ∘ using the Hu moments-based method to 3. 54 ∘. Similar results for the spinal and knee images can be reported. Furthermore, a large improvement of the 90 th percentile of absolute angular error by a factor of 3 indicates a better robustness and reduction of outliers for the proposed method.
CRIS-Team Scopus Importer:2023-11-03
Keywords: Hough Transform; Image Rotation; Machine Learning

Robust mixed one-bit compressive sensing - Huang, Xiaolin; Yang, Haiyan; Huang, Yixing; Shi, Lei; He, Fan; Maier, Andreas; Yan, Ming (Signal Processing, 2019)
When a measurement falls outside the quantization or measurable range, it becomes saturated and cannot be used in conventional signal recovery methods. Aiming at acquiring information from noisy saturated and regular measurements, we in this paper propose a new signal recovery method called mixed one-bit compressive sensing (M1bit-CS) and develop an efficient algorithm in the framework of alternating direction methods of multipliers. Numerical experiments on one-dimensional symmetric signals and two-dimensional image reconstruction from computed tomography verify the effectiveness of M1bit-CS on signal recovery from saturated measurements.
CRIS-Team Scopus Importer:2019-05-02
Keywords: Compressive sensing; Image reconstruction; One-bit; Signal recovery

Robust multi-contrast MRI denoising using trainable bilateral filters without noise-free targets - Pfaff, Laura; Wagner, Fabian; Hoßbach, Julian; Preuhs, Elisabeth; Maul, Noah; Thies, Mareike; Denzinger, Felix; Nickel, Marcel Dominik; Wuerfl, Tobias; Maier, Andreas (International Symposium on Biomedical Imaging (ISBI), 2023)

Robust Multiframe Super-Resolution Employing Iteratively Re-Weighted Minimization - Köhler, Thomas; Huang, Xiaolin; Schebesch, Frank; Aichert, André; Maier, Andreas; Hornegger, Joachim (IEEE Transactions on Computational Imaging, 2016)
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.robust

Robust Multi-Scale Anatomical Landmark Detection in Incomplete 3D-CT Data - Ghesu, Florin-Cristian; Georgescu, Bogdan; Grbic, Sasa; Maier, Andreas; Hornegger, Joachim; Comaniciu, Dorin (Medical Image Computing and Computer-Assisted Intervention MICCAI 2017, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.robust_1

Robust Multi-View 2-D/3-D Registration Using Point-To-Plane Correspondence Model - Schaffert, Roman; Wang, Jian; Fischer, Peter; Maier, Andreas; Borsdorf, Anja (IEEE Transactions on Medical Imaging, 2020)
In minimally invasive procedures, the clinician relies on image guidance to observe and navigate the operation site. In order to show structures which are not visible in the live X-ray images, such as vessels or planning annotations, X-ray images can be augmented with pre-operatively acquired images. Accurate image alignment is needed and can be provided by 2-D/3-D registration. In this paper, a multi-view registration method based on the point-to-plane correspondence model is proposed. The correspondence model is extended to be independent of the used camera coordinates and different multi-view registration schemes are introduced and compared. Evaluation is performed for a wide range of clinically relevant registration scenarios. We show for different applications that registration using correspondences from both views simultaneously provides accurate and robust registration, while the performance of the other schemes varies considerably. Our method also outperforms the state-of-the-art method for cerebral angiography registration, achieving a capture range of 18 mm and an accuracy of 0.22 & x00B1;0.07 mm. Furthermore, investigations on the minimum angle between the views are performed in order to provide accurate and robust registration, while minimizing the obstruction to the clinical workflow. We show that small angles around 30 & x00B0; are sufficient to provide reliable registration results.
CRIS-Team WoS Importer:2020-01-24

Robustness Investigation on Deep Learning CT Reconstruction for Real-Time Dose Optimization - Liu, Chang; Huang, Yixing; Maier, Joscha; Klein, Laura; Kachelriess, Marc; Maier, Andreas (Robustness Investigation on Deep Learning CT Reconstruction for Real-Time Dose Optimization, 2020)
In computed tomography (CT), automatic exposure control (AEC) is frequently used to reduce radiation dose exposure to patients. For organ-specific AEC, a preliminary CT reconstruction is necessary to estimate organ shapes for dose optimization, where only a few projections are allowed for real-time reconstruction. In this work, we investigate the performance of automated transform by manifold approximation (AUTOMAP) in such applications. For proof of concept, we investigate its performance on the MNIST dataset first, where the dataset containing all the 10 digits are randomly split into a training set and a test set. We train the AUTOMAP model for image reconstruction from 2 projections or 4 projections directly. The test results demonstrate that AUTOMAP is able to reconstruct most digits well with a false rate of 1.6% and 6.8% respectively. In our subsequent experiment, the MNIST dataset is split in a way that the training set contains 9 digits only while the test set contains the excluded digit only, for instance "2". In the test results, the digit "2"s are falsely predicted as "3" or "5" when using 2 projections for reconstruction, reaching a false rate of 94.4%. For the application in medical images, AUTOMAP is also trained on patients' CT images. The test images reach an average root-mean-square error of 290 HU. Although the coarse body outlines are well reconstructed, some organs are misshaped.<br
Keywords: Sparse-view reconstruction; deep learning; automatic exposure control; computed tomography

Robust Non-Rigid Registration through Agent-Based Action Learning - Krebs, Julian; Mansi, Tommaso; Delingette, Herve; Zhang, Li; Ghesu, Florin-Cristian; Miao, Shun; Maier, Andreas; Ayache, Nicholas; Liao, Rui; Kamen, Ali (Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.robust_0

Robust Parallel Speech Recognition in Multiple Energy Bands - Maier, Andreas; Hacker, Christian; Steidl, Stefan; Nöth, Elmar; Niemann, Heinrich (Pattern Recognition, 27th DAGM Synopsium, Vienna, Austria, August/September 2005, Proceedings, 2005)

Robust partial Fourier reconstruction for diffusion-weighted imaging using a recurrent convolutional neural network - Gadjimuradov, Fasil; Benkert, Thomas; Nickel, Marcel Dominik; Maier, Andreas (Magnetic Resonance in Medicine, 2021)
Purpose To develop an algorithm for robust partial Fourier (PF) reconstruction applicable to diffusion-weighted (DW) images with non-smooth phase variations. Methods Based on an unrolled proximal splitting algorithm, a neural network architecture is derived, which alternates between data consistency operations and regularization implemented by recurrent convolutions. In order to exploit correlations, multiple repetitions of the same slice are jointly reconstructed under consideration of permutation-equivariance. The algorithm is trained on DW liver data of 60 volunteers and evaluated on retrospectively and prospectively subsampled data of different anatomies and resolutions. Results The proposed method is able to significantly outperform conventional PF techniques on retrospectively subsampled data in terms of quantitative measures as well as perceptual image quality. In this context, joint reconstruction of repetitions as well as the particular type of recurrent network unrolling are found to be beneficial with respect to reconstruction quality. On prospectively PF-sampled data, the proposed method enables DW imaging with higher signal without sacrificing image resolution or introducing additional artifacts. Alternatively, it can be used to counter the TE increase in acquisitions with higher resolution. Furthermore, generalizability can be shown to prospective brain data exhibiting anatomies and contrasts not present in the training set. Conclusion This work demonstrates that robust PF reconstruction of DW data is feasible even at strong PF factors in anatomies prone to phase variations. Since the proposed method does not rely on smoothness priors of the phase but uses learned recurrent convolutions instead, artifacts of conventional PF methods can be avoided.
CRIS-Team WoS Importer:2021-12-10

Robust Quad-Tree based Registration on Whole Slide Images - Marzahl, Christian; Wilm, Frauke; Dressler, Franz F.; Tharun, Lars; Perner, Sven; Bertram, Christof A.; Kröger, Christine; Voigt, Jörn; Klopfleisch, Robert; Maier, Andreas; Aubreville, Marc; Breininger, Katharina (MICCAI Computational Pathlogy (COMPAY) Workshop, 2021)
The registration of whole slide images (WSIs) provides the basis for many subsequent processing steps in digital pathology. For instance, the registration of immunohistochemistry (IHC) and hematoxylin & eosin (H&E)-stained WSIs is usually the first step in guiding IHC diagnostic procedures. Still, many registration methods operate poorly on WSIs. Reasons for this include the WSI size, fluctuating image quality or elastic tissue deformations. Multiple prior methods are further specialised towards a specific image modality, such as histology or cytology, or rely on a specific preparation protocol. To minimise these effects, we developed a robust WSI registration, which differs from previous methods by the following new aspect: We introduce a multi-scale approach based on a quad-tree (QT), with several termination criteria that makes the algorithm particularly insensitive to tissue artefacts and that further allows to estimate a piece-wise affine transformation. We validated our method on five scanner systems and 60 WSIs with different stainings. Our results outperformed any publicly available WSI registration method. The QT code, WSI landmarks and tools used to create the validation dataset are made publicly available.<br

Robust Seed Mask Generation for Interactive Image Segmentation - Amrehn, Mario; Steidl, Stefan; Kowarschik, Markus; Maier, Andreas (2017 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC), 2017)
In interactive medical image segmentation, anatomical structures are extracted from reconstructed volumetric images. The first iterations of user interaction traditionally consist of drawing pictorial hints as an initial estimate of the object to extract. Only after this time consuming first phase, the efficient selective refinement of current segmentation results begins. Erroneously labeled seeds, especially near the border of the object, are challenging to detect and replace for a human and may substantially impact the overall segmentation quality. We propose an automatic seeding pipeline as well as a configuration based on saliency recognition, in order to skip the time-consuming initial interaction phase during segmentation. A median Dice score of 68.22% is reached before the first user interaction on the test data set with an error rate in seeding of only 0.088
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.robust_6

Robust Spectral Denoising for Water-Fat Separation in Magnetic Resonance Imaging - Lugauer, Felix; Nickel, Dominik; Wetzl, Jens; Kannengiesser, Stephan A. R.; Maier, Andreas; Hornegger, Joachim (18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part II, 2015)
Fat quantification based on the multi-echo Dixon method is gaining importance in clinical practice as it can match the accuracy of spectroscopy but provides high spatial resolution. Accurate quantification protocols, though, are limited to low SNR and suffer from a high noise bias. As the clinically relevant water and fat components are estimated by fitting a non-linear signal model to the data, the uncertainty is further amplified. In this work, we first establish the low-rank property and its locality assumptions for water-fat MRI and, consequently, propose a model-consistent but adaptive spectral denoising. A robust noise estimation in combination with a risk-minimizing threshold adds to a fully-automatic method. We demonstrate its capabilities on abdominal fat quantification data from in-vivo experiments. The denoising reduces the fit error on average by 37% and the uncertainty of the fat fraction by 58% in comparison to the original data while being edge-preserving.
Keywords: Robust Denoising; Water-Fat MRI; Locally Low-Rank

Robust Vessel Segmentation in Fundus Images - Budai, Attila; Bock, Rüdiger; Maier, Andreas; Hornegger, Joachim; Michelson, Georg (International Journal of Biomedical Imaging, 2013)
One of the most common modalities to examine the human eye is the eye-fundus photograph. The evaluation of fundus photographs is carried out by medical experts during time-consuming visual inspection. Our aim is to accelerate this process using computer aided diagnosis. As a first step, it is necessary to segment structures in the images for tissue differentiation. As the eye is the only organ, where the vasculature can be imaged in an in vivo and noninterventional way without using expensive scanners, the vessel tree is one of the most interesting and important structures to analyze. The quality and resolution of fundus images are rapidly increasing. Thus, segmentation methods need to be adapted to the new challenges of high resolutions. In this paper, we present a method to reduce calculation time, achieve high accuracy, and increase sensitivity compared to the original Frangi method. This method contains approaches to avoid potential problems like specular reflexes of thick vessels. The proposed method is evaluated using the STARE and DRIVE databases and we propose a new high resolution fundus database to compare it to the state-of-the-art algorithms. The results show an average accuracy above 94% and low computational needs. This outperforms state-of-the-art methods. © 2013 A. Budai et al.
UnivIS-Import:2015-03-09:Pub.2013.tech.IMMD.IMMD5.robust

ROCHADE: Robust Checkerboard Advanced Detection for Camera Calibration - Placht, Simon; Fürsattel, Peter; Assoumou Mengue, Etienne; Hofmann, Hannes; Schaller, Christian; Balda, Michael; Angelopoulou, Elli (Lecture Notes in Computer Science, 2014)
We present a new checkerboard detection algorithm which is able to detect checkerboards at extreme poses, or checkerboards which are highly distorted due to lens distortion even on low-resolution images. On the detected pattern we apply a surface fitting based subpixel refinement specifically tailored for checkerboard X-junctions. Finally, we investigate how the accuracy of a checkerboard detector affects the overall calibration result in multi-camera setups. The proposed method is evaluated on real images captured with different camera models to show its wide applicability. Quantitative comparisons to OpenCV's checkerboard detector show that the proposed method detects up to 80% more checkerboards and detects corner points more accurately, even under strong perspective distortion as often present in wide baseline stereo setups. © 2014 Springer International Publishing.
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.rochad
Keywords: Checkerboard Detection; Lens Distortion; Low Resolution Sensors; Multi Camera Calibration; Saddle-Based Subpixel Refinement

Rule-Based Models for Risk Estimation and Analysis of In-hospital Mortality in Emergency and Critical Care - Haas, Oliver; Maier, Andreas; Rothgang, Eva (Frontiers in Medicine, 2021)
We propose a novel method that uses associative classification and odds ratios to predict in-hospital mortality in emergency and critical care. Manual mortality risk scores have previously been used to assess the care needed for each patient and their need for palliative measures. Automated approaches allow providers to get a quick and objective estimation based on electronic health records. We use association rule mining to find relevant patterns in the dataset. The odds ratio is used instead of classical association rule mining metrics as a quality measure to analyze association instead of frequency. The resulting measures are used to estimate the in-hospital mortality risk. We compare two prediction models: one minimal model with socio-demographic factors that are available at the time of admission and can be provided by the patients themselves, namely gender, ethnicity, type of insurance, language, and marital status, and a full model that additionally includes clinical information like diagnoses, medication, and procedures. The method was tested and validated on MIMIC-IV, a publicly available clinical dataset. The minimal prediction model achieved an area under the receiver operating characteristic curve value of 0.69, while the full prediction model achieved a value of 0.98. The models serve different purposes. The minimal model can be used as a first risk assessment based on patient-reported information. The full model expands on this and provides an updated risk assessment each time a new variable occurs in the clinical case. In addition, the rules in the models allow us to analyze the dataset based on data-backed rules. We provide several examples of interesting rules, including rules that hint at errors in the underlying data, rules that correspond to existing epidemiological research, and rules that were previously unknown and can serve as starting points for future studies.

Scale-Space Anisotropic Total Variation for Limited Angle Tomography - Huang, Yixing; Taubmann, Oliver; Huang, Xiaolin; Haase, Viktor; Lauritsch, Günter; Maier, Andreas (IEEE Transactions on Radiation and Plasma Medical Sciences, 2018)
This paper addresses streak reduction in limited angle tomography. Although the iterative reweighted total variation (wTV) algorithm reduces small streaks well, it is rather inept at eliminating large ones since total variation (TV) regularization is scale-dependent and may regard these streaks as homogeneous areas. Hence, the main purpose of this paper is to reduce streak artifacts at various scales. We propose the scale-space anisotropic total variation (ssaTV) algorithm, which is derived from wTV, in two different implementations. The first implementation (ssaTV-1) utilizes an anisotropic gradient-like operator which uses 2s neighboring pixels along the streaks’ normal direction at each scale s. The second implementation (ssaTV-2) makes use of anisotropic down-sampling and up-sampling operations, similarly oriented along the streaks’ normal direction, to apply TV regularization at various scales. Experiments on numerical and clinical data demonstrate that both ssaTV algorithms reduce streak artifacts more effectively and efficiently than wTV, particularly when using multiple scale
UnivIS-Import:2018-09-05:Pub.2018.tech.IMMD.IMMD5.scales
Keywords: limited angle tomography; total variation; scale-space; anisotropic

Scaling calibration in region of interest reconstruction with the 1D and 2D ATRACT algorithm - Xia, Yan; Dennerlein, Frank; Bauer, Sebastian; Hofmann, Hannes; Hornegger, Joachim; Maier, Andreas (International Journal of Computer Assisted Radiology and Surgery, 2014)
Purpose: Recently, a reconstruction algorithm for region of interest (ROI) imaging in C-arm CT was published, named Approximate Truncation Robust Algorithm for Computed Tomography (ATRACT). Even in the presence of substantial data truncation, the algorithm is able to reconstruct images without the use of explicit extrapolation or prior knowledge. However, the method suffers from a scaling and offset artifact in the reconstruction. Hence, the reconstruction results are not quantitative. It is our goal to reduce the scaling and offset artifact so that Hounsfield unit (HU) values can be used for diagnosis. Methods: In this paper, we investigate two variants of the ATRACT method and present the analytical derivations of these algorithms in the Fourier domain. Then, we propose an empirical correction measure that can be applied to the ATRACT algorithm, to effectively compensate the scaling and offset issue. The proposed method is evaluated on ten clinical datasets in the presence of different degrees of artificial truncation. Results: With the proposed correction approach, we achieved an average relative root-mean-square error (rRMSE) of 2.81 % with respect to non-truncated Feldkamp, Davis, and Kress reconstruction, even for severely truncated data. The rRMSE is reduced to as little as 10 % of the image reconstructed without the scaling calibration. Conclusions: The reconstruction results show that ROI reconstruction of high accuracy can be achieved since the scaling and offset artifact are effectively eliminated by the proposed method. With this improvement, the HU values may be used for post-processing operations such as bone or soft tissue segmentation if some tolerance is accepted. © 2014 CARS.
UnivIS-Import:2015-03-09:Pub.2014.tech.IMMD.IMMD5.scalin
Keywords: Dose reduction; Region of interest imaging; Scaling calibration; Truncation correction

Scaling Calibration in the ATRACT Algorithm - Xia, Yan; Maier, Andreas; Dennerlein, Frank; Hornegger, Joachim (Proceedings Bildverarbeitung für die Medizin 2013, 2013)

Scanning trajectory optimisation using a quantitative Tuybased local quality estimation for robot-based X-ray computed tomography - Herl, Gabriel; Hiller, Jochen; Maier, Andreas (Nondestructive Testing and Evaluation, 2020)
Robotic CT systems allow complex scanning trajectories. This work presents a workflow to automatically calculate optimised scanning trajectories for robotic CT systems. In particular, as a local quality estimation, this work introduces a quantitative measure to quantify local reconstruction quality based on the Tuy conditions. The proposed method is tested in two summation experiments using an STL model of a motorcycle. In both experiments, a trajectory is calculated using a quantitative Tuy-based local quality estimation and the reconstruction result is then compared to reconstructions using conventional scanning trajectories. The comparison results indicate that the proposed approach automatically finds trajectories that enable 3D reconstructions with high image quality using much less projection data, which allows a significant reduction of scanning time.
CRIS-Team Scopus Importer:2020-07-10
Keywords: Computed tomography; image quality; local quality estimations; trajectory optimisation; Tuy conditions

ScanRacer – Fast Track Annotation of Segmentation Masks in Volumetric Medical Images - Diehm, Jessica; Hermann, Robert; Pertlwieser, Tobias; Cheng, Wei; Fu, Weilin; Kordon, Florian Johannes; Maier, Andreas (HEALTHINF - 14th International Conference on Health Informatics, 2021)
Introduction With the rise of deep learning [1], we see a dramatic need of curated and annotated medical image data. In particular, in volumetric images, such annotations are extremely costly, as each slice has to be outlined individually to generate ground truth for the training of deep learning algorithms. Recently, a new initiative was founded that aims at encouraging patients to donate their medical image data [2]. In particular, this initiative also asks for permission to crowd-source the data annotation. This forms a basis to generate sufficient data for large-scale training of deep learning algorithms in medical image analysis. Methods In order to encourage users to perform annotations, we explore gamification. In particular, we selected the setup of a racing game to generate an exciting user experience. The main idea is that the user is driving a race car across a volumetric image slice. In order to create tracks automatically, a simple segmentation method is required. For the first experiments, we chose thresholding to separate fore- and background. Then, we select the largest connected component and perform image processing to automatically extract a closed edge contour. As guidance, checkpoints are spread over the preliminary organ outline to guide the player in equidistant steps along the extracted contour. Based on edge detection [3], a score consisting of accumulated edge pixels is determined automatically as feedback for the player. In order to create a more challenging game experience, additional moving obstacles were added to the course. During driving the player creates a closed contour that is then transmitted from the game client to the server’s database. Results The game was implemented in Unity3D [4] and released as Android APK Installer (https://www.medicaldatadonors.org/index.php/scan-racer/). We chose Google Firebase to store segmentation results on the server (https://firebase.google.com). ScanRacer creates a challenging, yet rewarding experience for the user. Experienced players are able to create segmentation contours close to the correct segmentation outline. Yet detailed annotations still pose a challenge in this setup which will be addressed by the addition of further game elements. In contrast to many other serious games, the fun component of ScanRacer is very high as reported by test players. A gameplay teaser video demonstrates this in detail. (https://www.youtube.com/watch?v=JNmEGLCyf6w) Conclusion ScanRacer offers a unique experience for players for volumetric image segmentation. The game is was implemented in Unity3D and is available for free download. Sources can be shared at request. References [1] Maier, A., Syben, C., Lasser, T., & Riess, C. (2019). A gentle introduction to deep learning in medical image processing. Zeitschrift für Medizinische Physik, 29(2), 86-101. [2] Servadei, L., Schmidt, R., Eidelloth, C., & Maier, A. (2017, October). Medical Monkeys: A Crowdsourcing Approach to Medical Big Data. In OTM Confederated International Conferences" On the Move to Meaningful Internet Systems" (pp. 87-97). Springer, Cham. [3] Canny, J. (1986). A computational approach to edge detection. IEEE Transactions on pattern analysis and machine intelligence, (6), 679-698. [4] Murray, J. W. (2014). C# game programming cookbook for Unity 3D. AK Peters/CRC Press<br
Keywords: Gamification; Medical Image Annotation; Deep Learning

Scatter Correction for C-Arm CT Using Primary Modulation - Bier, Bastian; Müller, Kerstin; Berger, Martin; Choi, Jang-Hwan; Ritschl, Ludwig; Kachelriess, Marc; Fahrig, Rebecca; Maier, Andreas (Proceedings of the 4th International Conference on Image Formation in X-ray Computed Tomography, 2016)
UnivIS-Import:2018-09-11:Pub.2016.tech.IMMD.IMMD5.scatte_8

Scatter correction using a primary modulator on a clinical angiography C-arm CT system - Bier, Bastian; Berger, Martin; Maier, Andreas; Kachelriess, Marc; Ritschl, Ludwig; Müller, Kerstin; Choi, Jang-Hwan; Fahrig, Rebecca (Medical Physics, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.scatte
Keywords: C-Arm; CBCT; Primary Modulator; Scatter Correction

Schätzung von Faltungskernen für die Streusignalkorrektur in der Röntgenprojektionsradiographie aus mittels gewichteter Differenzen erzeugten Streustrahlbildern - Luckner, Christoph; Maier, Andreas; Dennerlein, Frank (Bildverarbeitung für die Medizin 2014, 2014)
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.schtzu
Keywords: scatter correction; projection; x-ray

Securing Collaborative Medical AI by Using Differential Privacy: Domain Transfer for Classification of Chest Radiographs - Tayebi Arasteh, Soroosh; Lotfinia, Mahshad; Nolte, Teresa; Saehn, Marwin-Jonathan; Isfort, Peter; Kuhl, Christiane; Nebelung, Sven; Kaissis, Georgios; Truhn, Daniel (Radiology: Artificial Intelligence, 2024)