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

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

Segmentation, Classification, and Visualization of Orca Calls Using Deep Learning - Schröter, Hendrik; Nöth, Elmar; Maier, Andreas; Cheng, Rachael; Barth, Volker; Bergler, Christian (ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019)
Audiovisual media are increasingly used to study the communication and behavior of animal groups, e.g. by placing microphones in the animals habitat resulting in huge datasets with only a small amount of animal interactions. The Orcalab has recorded orca whales since 1973 using stationary underwater hydrophones and made it publicly available on the Orchive. There exist over 15 000 manually extracted orca/noise annotations and about 20 000 h unseen audio data. To analyze the behavior and communication of killer whales we need to interpret the different call types. In this work, we present a two-stage classification approach using the labeled call/noise files and a few labeled call-type files. Results indicate a reliable accuracy of 95.0 % for call segmentation and 87 % for classification of 12 call classes. We further visualize the learned orca call representations in the convolutional neural network (CNN) activations to explain the potential of CNN based recognition for bioaccousitc signal
Keywords: orca; bioacousitc signals; CNN; classification; visualization

Segmentation of Acute Ischemic Stroke in Native and Enhanced CT using Uncertainty-aware Labels - Vorberg, Linda; Taubmann, Oliver; Ditt, Hendrik; Maier, Andreas (Informatik aktuell, 2024)
In stroke diagnosis, a non-contrast CT (NCCT) is the first scan acquired and bears the possibility to identify ischemic changes in the brain. Their identification and segmentation are subject to high inter-rater variability. We develop and evaluate models based on labels that reflect the uncertainty in segmentation hypotheses by annotation of minimum (“inner”) and maximum (“outer”) contours of perceived presence of infarct core and hypoperfused tissue. These labels are used for training nnU-Net to segment both from NCCT and CT angiography (CTA) scans of 167 patients. The predicted output is post-processed to obtain delineations of the tissue of interest at varying distances between inner and outer contours. Compared to the ground truth, infarcts of medium size (10 to 70 ml) could be segmented in the NCCT scans with a median error of 3.7 ml (6.2 ml for CTA) of excess predicted volume, missing 6.4 ml (3.5 ml) of the infarct.
CRIS-Team Scopus Importer:2024-03-29

Segmentation of Fat and Fascias in Canine Ultrasound Images - Rybakov, Oleksiy; Stromer, Daniel; Mischewski, Irina; Maier, Andreas (Bildverarbeitung für die Medizin 2018 (BVM 2018), 2018)
The connective tissue between fat and muscle termed fascia has been of interest to the recent clinical and biological research. However, in the canine and human medicine, the anatomic knowledge is still limited. To analyze the superficial fascia in canine medicine, a database with around 200 ultrasound images of one dog has been created. The superficial fascia contains fat compartments and is closely connected to the surrounding structures such as the skin's dermis and the epimysium of the muscles. This work proposes a semi-automatic and fully-automatic segmentation algorithm separating the different layers of ultrasound images of canines. Both algorithms were evaluated on a set of 24 expert-labeled images achieving high accuracy scores up to 95.9
Keywords: fascia; segmentation; ultrasound; canine; fat; open-source; GUI

Segmentation of Photovoltaic Module Cells in Uncalibrated Electroluminescence Images - Deitsch, Sergiu; Buerhop-Lutz, Claudia; Sovetkin, Evgenii; Steland, Ansgar; Gallwitz, Florian; Maier, Andreas; Rieß, Christian (Machine Vision and Applications, 2021)
High resolution electroluminescence (EL) images captured in the infrared spectrum allow to visually and non-destructively inspect the quality of photovoltaic (PV) modules. Currently, however, such a visual inspection requires trained experts to discern different kind of defects, which is time-consuming and expensive. In this work, we propose a robust automated segmentation method for extraction of individual solar cells from EL images of PV modules. Automated segmentation of cells is a key step in automating the visual inspection workflow. It also enables controlled studies on large amounts of data to understanding the effects of module degradation over time-a process not yet fully understood. The proposed method infers in several steps a high-level solar module representation from low-level edge features. An important step in the algorithm is to formulate the segmentation problem in terms of lens calibration by exploiting the plumbline constraint. We evaluate our method on a dataset of various solar modules types containing a total of 408 solar cells with various defects. Our method robustly solves this task with a median weighted Jaccard index of 94.47% and an F1 score of 97.62%, both indicating a very high similarity between automatically segmented and ground truth solar cell masks.<br
Keywords: Photovoltaic modules; Electroluminescence imaging; visual inspection; lens distortion; solar cell extraction

Segmentation of the Carotid Lumen and Vessel Wall using Deep Learning and Location Priors - Thamm, Florian; Denzinger, Felix; Rist, Leonhard; Martín Vicario, Celia; Kordon, Florian Johannes; Maier, Andreas (2021)
Keywords: Vessel Wall Segmentation; Magnetic Resonance Imaging; Deep Learning

Self-Calibration and Simultaneous Motion Estimation for C-arm CT using Fiducial Markers - Syben-Leisner, Christopher; Bier, Bastian; Berger, Martin; Aichert, André; Fahrig, Rebecca; Gold, Garry; Levenston, Marc; Maier, Andreas (Bildverarbeitung für die Medizin 2017: Algorithmen - Systeme - Anwendungen., 2017)
C-arm cone-beam CT systems have an increasing popularity in the clinical environment due to their highly flexible scan trajectories. Recent work used these systems to acquire images of the knee joint under weight-bearing conditions. During the scan, the patient is in a standing or in a squatting position and is likely to show involuntary motion, which corrupts image reconstruction. The state-of-the-art fully automatic motion compensation relies on fiducial markers for motion estimation. Due to the not reproducible horizontal trajectory, the system has to be calibrated with a calibration phantom before or after each scan. In this work we present a method to incorporate a self-calibration into the existing motion compensation framework without the need of prior geometric calibration. Quantitative and qualitative evaluations on a numerical phantom as well as clinical data, show superior results compared to the current state-of-the-art method. Moreover, the clinical workflow is improved, as a dedicated system calibration for weight-bearing acquisitions is no longer required.
UnivIS-Import:2018-09-06:Pub.2017.tech.IMMD.IMMD5.selfca_1

Self-Supervised 2D/3D Registration for X-Ray to CT Image Fusion - Jaganathan, Srikrishna; Kukla, Maximilian; Wang, Jian; Shetty, Karthik; Maier, Andreas (2023)
Deep Learning-based 2D/3D registration enables fast, robust, and accurate X-ray to CT image fusion when large annotated paired datasets are available for training. However, the need for paired CT volume and X-ray images with ground truth registration limits the applicability in interventional scenarios. An alternative is to use simulated X-ray projections from CT volumes, thus removing the need for paired annotated datasets. Deep Neural Networks trained exclusively on simulated X-ray projections can perform significantly worse on real X-ray images due to the domain gap. We propose a self-supervised 2D/3D registration framework combining simulated training with unsupervised feature and pixel space domain adaptation to overcome the domain gap and eliminate the need for paired annotated datasets. Our framework achieves a registration accuracy of 1.83 ± 1.16 mm with a high success ratio of 90.1% on real X-ray images showing a 23.9% increase in success ratio compared to reference annotation-free algorithms.<br
Keywords: Training; Representation learning; Deep learning; Computer vision; Computed tomography; Neural networks; Self-supervised learning

Self-Supervised Learning of Domain-Invariant Local Features for Robust Visual Localization under Challenging Conditions - Venator, Moritz; El Himer, Yassine; Aklanoglu, Selcuk; Bruns, Erich; Maier, Andreas (IEEE Robotics and Automation Letters, 2021)
Visual localization provides the basis for many robotics applications such as autonomous navigation or augmented reality. Especially in outdoor scenes, robust localization requires local features which can be reliably extracted and matched under changing conditions. Previous approaches have applied generative image-to-image translation models to align images in a single domain before correspondence search. In this paper, we invert the concept and elaborate why it is more promising to use image domain adaptation for training of robust local features. Integrating this idea into a self-supervised training framework, we show in various experiments covering image matching, visual localization, and scene reconstruction that our Domain-Invariant SuperPoint (DISP) outperforms existing self-supervised methods in terms of repeatability, generalization, and robustness. In contrast to competitive supervised local features, our modular and fully self-supervised approach can be easily adapted to different domains and localization tasks as it does not require ground truth correspondences for trainin
Keywords: Feature extraction; Location awareness; Training; Visualization; Task analysis; Detectors; Image reconstruction

Self-supervised MRI denoising: leveraging Stein’s unbiased risk estimator and spatially resolved noise maps - Pfaff, Laura; Hoßbach, Julian; Preuhs, Elisabeth; Wagner, Fabian; Arroyo Camejo, Silvia; Kannengiesser, Stephan; Nickel, Dominik; Wuerfl, Tobias; Maier, Andreas (Scientific Reports, 2023)
Thermal noise caused by the imaged object is an intrinsic limitation in magnetic resonance imaging (MRI), resulting in an impaired clinical value of the acquisitions. Recently, deep learning (DL)-based denoising methods achieved promising results by extracting complex feature representations from large data sets. Most approaches are trained in a supervised manner by directly mapping noisy to noise-free ground-truth data and, therefore, require extensive paired data sets, which can be expensive or infeasible to obtain for medical imaging applications. In this work, a DL-based denoising approach is investigated which operates on complex-valued reconstructed magnetic resonance (MR) images without noise-free target data. An extension of Stein’s unbiased risk estimator (SURE) and spatially resolved noise maps quantifying the noise level with pixel accuracy were employed during the training process. Competitive denoising performance was achieved compared to supervised training with mean squared error (MSE) despite optimizing the model without noise-free target images. The proposed DL-based method can be applied for MR image enhancement without requiring noise-free target data for training. Integrating the noise maps as an additional input channel further enables the regulation of the desired level of denoising to adjust to the preference of the radiologist.
CRIS-Team Scopus Importer:2023-12-29

Semi-Automatic Algorithm for Breast MRI Lesion Segmentation Using Marker-Controlled Watershed Transformation - Vesal, Sulaiman; Diaz-Pinto, Andres; Ravikumar, Nishant; Ellmann, Stephan; Davari, Amirabbas; Maier, Andreas (2017 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC), 2017)
UnivIS-Import:2018-09-11:Pub.2017.tech.IMMD.IMMD5.semiau_5

Semi-Automatic Basket Catheter Reconstruction from Two X-Ray Views - Zhong, Xia; Hoffmann, Matthias; Strobel, Norbert; Maier, Andreas (Pattern Recognition, 2015)
Ablation guided by focal impulse and rotor mapping (FIRM) is a novel treatment option for atrial fibrillation, a frequent heart arrhythmia. This procedure is performed minimally invasively and at least partially under fluoroscopic guidance. It involves a basket catheter comprising 64 electrodes. The 3-D position of these electrodes is important during treatment. We propose a novel model-based method for 3-D reconstruction of this catheter using two X-ray images taken from different views. Our approach requires only little user interaction. An evaluation of the method found that the electrodes of the basket catheter can be reconstructed with a median error of 1.5 mm for phantom data and 3.4 mm for clinical data.
UnivIS-Import:2017-12-18:Pub.2015.tech.IMMD.IMMD5.semiau_8

Semi-Automatic Cell Correspondence Analysis Using Iterative Point Cloud Registration - Chen, Shuqing; Gehrer, Simone; Kaliman, Sara; Ravikumar, Nishant; Becit, Abdurrahman; Aliee, Maryam; Dudziak, Diana; Merkel, Rudolf; Smith, Ana-Suncana; Maier, Andreas (Bildverarbeitung für die Medizin 2019. Informatik aktuell., 2019)

Semi-Permeable Filters for Interior Region of Interest Dose Reduction in X-Ray Microscopy - Huang, Yixing; Mill, Leonid; Stoll, Robert; Kling, Lasse; Aust, Oliver; Wagner, Fabian; Grüneboom, Anika; Schett, Georg; Christiansen, Silke H.; Maier, Andreas (Bildverarbeitung für die Medizin, 2021)
In osteoporosis research, the number and size of lacunae in cortical bone tissue are important characteristics of osteoporosis development. In order to reconstruct lacunae well in X-ray microscopy while protecting bone marrow from high-dose damage in in-vivo experiments, semi-permeable X-ray filters are proposed for dose reduction. Compared with an opaque filter, image quality with a semi-permeable filter is improved remarkably. For image reconstruction, both iterative reconstruction with reweighted total variation (wTV) and FDK reconstruction from penalized weighted least-square (PWLS) processed projections can reconstruct lacunae when the transmission rate of the filter is as small as 5%. However, PWLS is superior in computation efficiency.<br
Keywords: Osteoporosis research; X-ray filter; X-ray microscopy; dose reduction

SEMPAI: a Self-Enhancing Multi-Photon Artificial Intelligence for Prior-Informed Assessment of Muscle Function and Pathology - Mühlberg, Alexander; Ritter, Paul; Langer, Sabine; Goossens, Chloë; Nübler, Stefanie; Schneidereit, Dominik; Taubmann, Oliver; Denzinger, Felix; Nörenberg, Dominik; Haug, Michael; Schürmann, Sebastian; Horstmeyer, Roarke; et al. (Advanced Science, 2023)
Deep learning (DL) shows notable success in biomedical studies. However, most DL algorithms work as black boxes, exclude biomedical experts, and need extensive data. This is especially problematic for fundamental research in the laboratory, where often only small and sparse data are available and the objective is knowledge discovery rather than automation. Furthermore, basic research is usually hypothesis-driven and extensive prior knowledge (priors) exists. To address this, the Self-Enhancing Multi-Photon Artificial Intelligence (SEMPAI) that is designed for multiphoton microscopy (MPM)-based laboratory research is presented. It utilizes meta-learning to optimize prior (and hypothesis) integration, data representation, and neural network architecture simultaneously. By this, the method allows hypothesis testing with DL and provides interpretable feedback about the origin of biological information in 3D images. SEMPAI performs multi-task learning of several related tasks to enable prediction for small datasets. SEMPAI is applied on an extensive MPM database of single muscle fibers from a decade of experiments, resulting in the largest joint analysis of pathologies and function for single muscle fibers to date. It outperforms state-of-the-art biomarkers in six of seven prediction tasks, including those with scarce data. SEMPAI's DL models with integrated priors are superior to those without priors and to prior-only approaches.
CRIS-Team Scopus Importer:2023-08-25
Keywords: deep learning; explainable artificial intelligence; meta-learning; multiphoton microscopy; muscle research; prior information integration; scientific machine learning

Severe MR Motion Artefact Correction with 2 step Deep Learning-based guidance - Hoßbach, Julian; Splitthoff, Daniel Nicolas; Cauley, Stephen; Clifford, Bryan; Polak, Daniel; Maier, Andreas (2023 ISMRM & ISMRT Annual Meeting & Exhibition, 2023)

Shading Correction for Grating-based Differential Phase Contrast X-ray Imaging - Käppler, Sebastian; Wandner, Johannes; Weber, Thomas; Maier, Andreas; Anton, Gisela; Hornegger, Joachim; Riess, Christian (IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), 2014)

SHAMANN: Shared Memory Augmented Neural Networks - Bercea, Cosmin I.; Pauly, Olivier; Maier, Andreas; Ghesu, Florin C. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2019)
Current state-of-the-art methods for semantic segmentation use deep neural networks to learn the segmentation mask from the input image signal as an image-to-image mapping. While these methods effectively exploit global image context, the learning and computational complexities are high. We propose shared memory augmented neural network actors as a dynamically scalable alternative. Based on a decomposition of the image into a sequence of local patches, we train such actors to sequentially segment each patch. To further increase the robustness and better capture shape priors, an external memory module is shared between different actors, providing an implicit mechanism for image information exchange. Finally, the patch-wise predictions are aggregated to a complete segmentation mask. We demonstrate the benefits of the new paradigm on a challenging lung segmentation problem based on X-Ray images, as well as on two synthetic tasks based on MNIST. On the X-Ray data, our method achieves state-of-the-art accuracy with a significantly reduced model size, 3–5 times compared to reference methods. In addition, we reduce the number of failure cases by at least half.
CRIS-Team Scopus Importer:2019-06-04

Sharp as a Tack: Measuring and Comparing Edge Sharpness in Motion-Compensated Medical Image Reconstruction - Taubmann, Oliver; Wetzl, Jens; Lauritsch, Günter; Maier, Andreas; Hornegger, Joachim (Bildverarbeitung für die Medizin 2015, 2015)
UnivIS-Import:2015-04-17:Pub.2015.tech.IMMD.IMMD5.sharpa_0

Sheet metal forming limits as classification problem - Jaremenko, Christian; Huang, Xiaolin; Affronti, Emanuela; Merklein, Marion; Maier, Andreas (Proceedings of the 15th IAPR International Conference on Machine Vision Applications, 2017)
Forming limit diagrams are used to evaluate the formability of metal sheets and describe the maximum strain to failure in terms of major and minor strain. The main idea is the detection of the onset of necking in limited areas of the investigated sheet metals. Current methods introduce location-or time-dependency or may require user interaction. Within this contribution we interpret the onset of necking as classification problem and show that a support vector machine performs comparable to a human experts group. Best results reach up to 87.5 % avg. precision and 84.9 % avg. recall, using all available 2D-information, while being time- and location-independent.
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.sheetm

SIFT Key-Points for Lung Tumor Detection in PET/CT Images - Nitschke, Marlies; Jian, Tian; Bayer, Siming; Ren, Qiushi; Maier, Andreas (Medical Image Understanding and Analysis (MIUA), 2017)
Automatic tumor segmentation enables a fast, reliable and reproducible diagnostic to deliver best treatment procedures for cancer patients. However, state-of-the-art tumor segmentation algorithms are based on manual input which is time- and work-consuming. Furthermore, varying inputs can lead to different segmentation results. To simplify the workflow, a tumor detection algorithm should be used instead of manual input. In this preliminary study, a tumor detection algorithm was investigated and validated for six PET/CT data sets of lung tumor patients. Scale-invariant feature transform (SIFT) keypoints were extracted as tumor candidates in PET images. For each candidate point, intensity, scale and orientation invariant features were calculated both from the PET images and registered CT images. Based on these features, the keypoints were classified either as tumor or as background with a neural network. The neural network classification showed an AUC of 0.71. The tumor detection ratio was 78.6 %. The result of the evaluation with clinical data demonstrated the high potential of the presented lung tumor detection algorithm. We are convinced, further developments of this approach will result in enhanced cancer detection in the entire body.<br

Signal decomposition for X-ray dark-field imaging - Käppler, Sebastian; Bayer, Florian; Weber, Thomas; Maier, Andreas; Anton, Gisela; Hornegger, Joachim; Beckmann, Matthias; Fasching, Peter; Hartmann, Arndt; Heindl, Felix; Michel, Thilo; Oezguel, Gueluemser; et al. (MICCAI 2014, 2014)
Grating-based X-ray dark-field imaging is a new imaging modality. It allows the visualization of structures at micrometer scale due to small-angle scattering of the X-ray beam. However, reading darkfield images is challenging as absorption and edge-diffraction effects also contribute to the dark-field signal, without adding diagnostic value. In this paper, we present a novel–and to our knowledge the first–algorithm for isolating small-angle scattering in dark-field images, which greatly improves their interpretability. To this end, our algorithm utilizes the information available from the absorption and differential phase images to identify clinically irrelevant contributions to the dark-field image. Experimental results on phantom and ex-vivo breast data promise a greatly enhanced diagnostic value of dark-field image
EVALuna2:6632

Simulation-driven training of vision transformers enables metal artifact reduction of highly truncated CBCT scans - Fan, Fuxin; Ritschl, Ludwig; Beister, Marcel; Biniazan, Ramyar; Wagner, Fabian; Kreher, Björn W.; Gottschalk, Tristan; Kappler, Steffen; Maier, Andreas (Medical Physics, 2023)
Background: Due to the high attenuation of metals, severe artifacts occur in cone beam computed tomography (CBCT). The metal segmentation in CBCT projections usually serves as a prerequisite for metal artifact reduction algorithms. Purpose: The occurrence of truncation caused by the limited detector size leads to the incomplete acquisition of metal masks from the threshold-based method in CBCT volume. Therefore, segmenting metal directly in CBCT projections is pursued in this work. Methods: Since the generation of high quality clinical training data is a constant challenge, this study proposes to generate simulated digital radiographs (data I) based on real CT data combined with self-designed computer aided design (CAD) implants. In addition to the simulated projections generated from 3D volumes, 2D X-ray images combined with projections of implants serve as the complementary data set (data II) to improve the network performance. In this work, SwinConvUNet consisting of shift window (Swin) vision transformers (ViTs) with patch merging as encoder is proposed for metal segmentation. Results: The model's performance is evaluated on accurately labeled test datasets obtained from cadaver scans as well as the unlabeled clinical projections. When trained on the data I only, the convolutional neural network (CNN) encoder-based networks UNet and TransUNet achieve only limited performance on the cadaver test data, with an average dice score of 0.821 and 0.850. After using both data II and data I during training, the average dice scores for the two models increase to 0.906 and 0.919, respectively. By replacing the CNN encoder with Swin transformer, the proposed SwinConvUNet reaches an average dice score of 0.933 for cadaver projections when only trained on the data I. Furthermore, SwinConvUNet has the largest average dice score of 0.953 for cadaver projections when trained on the combined data set. Conclusions: Our experiments quantitatively demonstrate the effectiveness of the combination of the projections simulated under two pathways for network training. Besides, the proposed SwinConvUNet trained on the simulated projections performs state-of-the-art, robust metal segmentation as demonstrated on experiments on cadaver and clinical data sets. With the accurate segmentations from the proposed model, metal artifact reduction can be conducted even for highly truncated CBCT scans. <br
Keywords: data argumentation; swin vision transformer; metal artifact reduction; metal segmentation

Simulation study on X-ray phase contrast imaging with dual-phase gratings - Bopp, Johannes; Ludwig, Veronika; Seifert, Maria; Pelzer, Georg; Maier, Andreas; Anton, Gisela; Riess, Christian (International Journal of Computer Assisted Radiology and Surgery, 2019)
Keywords: Dual-phase grating; Grating-based interferometry; Phase contrast imaging; Talbot–Lau

Simultaneous Estimation of X-Ray Back-Scatter and Forward-Scatter Using Multi-task Learning - Roser, Philipp; Zhong, Xia; Birkhold, Annette; Preuhs, Alexander; Syben-Leisner, Christopher; Hoppe, Elisabeth; Strobel, Norbert; Kowarschik, Markus; Fahrig, Rebecca; Maier, Andreas (Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 2020)
Scattered radiation is a major concern impacting X-ray image-guided procedures in two ways. First, back-scatter significantly contributes to patient (skin) dose during complicated interventions. Second, forward-scattered radiation reduces contrast in projection images and introduces artifacts in 3-D reconstructions. While conventionally employed anti-scatter grids improve image quality by blocking X-rays, the additional attenuation due to the anti-scatter grid at the detector needs to be compensated for by a higher patient entrance dose. This also increases the room dose affecting the staff caring for the patient. For skin dose quantification, back-scatter is usually accounted for by applying pre-determined scalar back-scatter factors or linear point spread functions to a primary kerma forward projection onto a patient surface point. However, as patients come in different shapes, the generalization of conventional methods is limited. Here, we propose a novel approach combining conventional techniques with learning-based methods to simultaneously estimate the forward-scatter reaching the detector as well as the back-scatter affecting the patient skin dose. Knowing the forward-scatter, we can correct X-ray projections, while a good estimate of the back-scatter component facilitates an improved skin dose assessment. To simultaneously estimate forward-scatter as well as back-scatter, we propose a multi-task approach for joint back- and forward-scatter estimation by combining X-ray physics with neural networks. We show that, in theory, highly accurate scatter estimation in both cases is possible. In addition, we identify research directions for our multi-task framework and learning-based scatter estimation in general.<br
Keywords: X-ray scatter Skin dose Multi-task learning

Simultaneous Generation of X-ray and Range Images using XCAT under Motion - Rybakov, Oleksiy; Bier, Bastian; Maier, Jennifer; Unberath, Mathias; Maier, Andreas (2017 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC), 2017)
Novel algorithms in the field of X-ray imaging are commonly evaluated on simulation software first, before they are implemented on a clinical scanner in order to test their performance in a very controlled setup. This reduces patient dose and facilitates the development of new approaches and methods. In recent years, range imaging applications were established in the field of medical imaging. Range imaging showed its potential in radiotherapy, augmented reality, and, recently, also for motion correction in cone-beam computed tomography. In this work, we present an open-source software tool that allows to generate X-ray projections and surface information in one step completely on the graphics processing unit. To this end, we extend a state-of-the-art rendering method based on an append buffer. Arbitrary imaging geometries for the X-ray source and the range imaging camera can be selected. We test the proposed method on the XCAT phantom in two fundamentally different scenarios: a weight-bearing acquisition showing a squat, and a supine acquisition of a breathing patien
Keywords: range imaging; x-ray imaging; append buffer; weight-bearing; open-source; XCAT

Simultaneous reconstruction of multiple stiff wires from a single X-ray projection for endovascular aortic repair - Breininger, Katharina; Hanika, Moritz; Weule, Mareike; Kowarschik, Markus; Pfister, Marcus; Maier, Andreas (International Journal of Computer Assisted Radiology and Surgery, 2019)
Purpose : Endovascular repair of aortic aneurysms (EVAR) can be supported by fusing pre- and intraoperative data to allow for improved navigation and to reduce the amount of contrast agent needed during the intervention. However, stiff wires and delivery devices can deform the vasculature severely, which reduces the accuracy of the fusion. Knowledge about the 3D position of the inserted instruments can help to transfer these deformations to the preoperative information. Method : We propose a method to simultaneously reconstruct the stiff wires in both iliac arteries based on only a single monoplane acquisition, thereby avoiding interference with the clinical workflow. In the available X-ray projection, the 2D course of the wire is extracted. Then, a virtual second view of each wire orthogonal to the real projection is estimated using the preoperative vessel anatomy from a computed tomography angiography as prior information. Based on the real and virtual 2D wire courses, the wires can then be reconstructed in 3D using epipolar geometry. Results : We achieve a mean modified Hausdorff distance of 4.2 mm between the estimated 3D position and the true wire course for the contralateral side and 4.5 mm for the ipsilateral side. Conclusion : The accuracy and speed of the proposed method allow for use in an intraoperative setting of deformation correction for EVAR.
CRIS-Team Scopus Importer:2019-10-22
Keywords: EVAR; Fluoroscopy; Guide wire reconstruction; Image guidance

Single-breath-hold 3-D CINE imaging of the left ventricle using Cartesian sampling - Wetzl, Jens; Schmidt, Michaela; Pontana, François; Longère, Benjamin; Lugauer, Felix; Maier, Andreas; Hornegger, Joachim; Forman, Christoph (Magnetic Resonance Materials in Physics Biology and Medicine, 2017)
UnivIS-Import:2017-07-10:Pub.2017.tech.IMMD.IMMD5.single_7
Keywords: 3-D CINE imaging; Compressed sensing; Ventricular function

Single-breath-hold abdominal T1 mapping using 3D Cartesian Look-Locker with spatiotemporal sparsity constraints - Lugauer, Felix; Wetzl, Jens; Forman, Christoph; Schneider, Manuel; Kiefer, Berthold; Hornegger, Joachim; Nickel, Dominik; Maier, Andreas (Magnetic Resonance Materials in Physics Biology and Medicine, 2018)
UnivIS-Import:2018-09-05:Pub.2018.tech.IMMD.IMMD5.single
Keywords: T1 mapping; 3D Look-Locker; Compressed Sensing; Poisson Sampling

Single Breath-Hold Abdominal T1 Mapping Using 3-D Cartesian Sampling and Spatiotemporally Constrained Reconstruction - Lugauer, Felix; Wetzl, Jens; Forman, Christoph; Schneider, Manuel; Kiefer, Berthold; Nickel, Dominik; Maier, Andreas (Proceedings of the 25th Annual Meeting of the ISMRM (ISMRM 2017), 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.single_2
Keywords: Volumetric T1 mapping; Look-Locker; Compressed Sensing

Single Material Beam Hardening Correction in CT Using an Analytical Energy Response Model - Haase, Viktor; Hahn, Katharina; Schoendube, Harald; Stierstorfer, Karl; Maier, Andreas; Noo, Frédéric (2020 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), 2020)
Material decomposition algorithms for dual energy CT require knowledge on the energy response of the used system. This knowledge can be gained either through calibration measurements or using an explicit analytical model. While the former approach is preferred by vendors, it tends to be intimately linked with the chosen image reconstruction approach and is thus not easily amenable to using other, potentially more advanced, image reconstruction methods. Models for the detector response and the source spectrum have been published in the literature. Here, we investigate how satisfactory these general models are to represent the characteristics of one specific scanner. Specifically, the models are tested by using them to perform a water-based, single material beam hardening correction on real CT data. This test is performed using an anthropomorphic head phantom and the ACR CT accreditation phantom both scanned at two different X-ray tube settings that are typically used for dual energy CT. The scans are performed using a state-of-the-art diagnostic CT scanner (SOMATOM Drive, Siemens Healthcare GmbH, Germany). The images corrected for beam hardening using the models are compared with the classical reconstructions provided by the vendor, for which beam hardening correction is based on real measurements from a calibration phantom. The study demonstrates that the results provided by the vendor can be reproduced with high accuracy (within 1-3 HU in mean value) provided some analytical correction for forward scatter is included as well.<br
Keywords: Beam Hardening; Dual Energy CT; Source Spectrum; Detector Response

Single-material beam hardening correction via an analytical energy response model for diagnostic CT - Haase, Viktor; Hahn, Katharina; Schoendube, Harald; Stierstorfer, Karl; Maier, Andreas; Noo, Frederic (Medical Physics, 2022)
Background Various clinical studies show the potential for a wider quantitative role of diagnostic X-ray computed tomography (CT) beyond size measurements. Currently, the clinical use of attenuation values is, however, limited due to their lack of robustness. This issue can be observed even on the same scanner across patient size and positioning. There are different causes for the lack of robustness in the attenuation values; one possible source of error is beam hardening of the X-ray source spectrum. The conventional and well-established approach to address this issue is a calibration-based single material beam hardening correction (BHC) using a water cylinder. Purpose We investigate an alternative approach for single-material BHC with the aim of producing a more robust result for the attenuation values. The underlying hypothesis of this investigation is that calibration-based BHC automatically corrects for scattered radiation in a manner that is suboptimal in terms of bias as soon as the scanned object strongly deviates from the water cylinder used for calibration. Methods The approach we propose performs BHC via an analytical energy response model that is embedded into a correction pipeline that efficiently estimates and subtracts scattered radiation in a patient-specific manner prior to BHC. The estimation of scattered radiation is based on minimizing, in average, the squared difference between our corrected data and the vendor-calibrated data. The used energy response model is considering the spectral effects of the detector response and the prefiltration of the source spectrum, including a beam-shaping bowtie filter. The performance of the correction pipeline is first characterized with computer simulated data. Afterward, it is tested using real 3-D CT data sets of two different phantoms, with various kV settings and phantom positions, assuming a circular data acquisition. The results are compared in the image domain to those from the scanner. Results For experiments with a water cylinder, the proposed correction pipeline leads to similar results as the vendor. For reconstructions of a QRM liver phantom with extension ring, the proposed correction pipeline achieved a more uniform and stable outcome in the attenuation values of homogeneous materials within the phantom. For example, the root mean squared deviation between centered and off-centered phantom positioning was reduced from 6.6 to 1.8 HU in one profile. Conclusions We have introduced a patient-specific approach for single-material BHC in diagnostic CT via the use of an analytical energy response model. This approach shows promising improvements in terms of robustness of attenuation values for large patient sizes. Our results contribute toward improving CT images so as to make CT attenuation values more reliable for use in clinical practice.
CRIS-Team WoS Importer:2022-07-01

SkinNet: A Deep Learning Framework for Skin Lesion Segmentation - Vesal, Sulaiman; Ravikumar, Nishant; Maier, Andreas (IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC), 2018)
UnivIS-Import:2019-08-15:Pub.2018.tech.IMMD.IMMD5.skinne_5

Skinscan: 3D Dermatologic Diagnosis and Documentation with Commodity Devices - Nau, Merlin; Schiffers, Florian; Li, Yunhao; Xu, Bingjie; Maier, Andreas; Tumblin, Jack; Walton, Marc; Katsaggelos, Aggelos K; Willomitzer, Florian; Cossairt, Oliver (Asilomar Proceedings, 2022)
Computational photography has become a valuable field of interest for dermatologists. Current telemedical approaches mainly use two-dimensional (2D) color imagery, which cannot always capture sufficient information for the physician. In clinical practice dedicated imaging systems can acquire valuable three-dimensional (3D) data to improve the patients outcome. The major drawback of clinical imaging systems is the price and the availability to the broad audience. We tackle this problem by providing an open-source software framework. The framework uses a photometric stereo-inspired gradient illumination technique, enabling detailed 3D measurements of fine skin structures, like fingerprints. Especially in vastly populated or underdeveloped areas, our system could show benefits for initial diagnosis and documenting the evolution of skin disease

Skinscan: Low-Cost 3D-Scanning for Dermatologic Diagnosis and Documentation - Nau, Merlin; Schiffers, Florian; Li, Yunhao; Xu, Bingjie; Maier, Andreas; Tumblin, Jack; Walton, Marc; Katsaggelos, Aggelos K; Willomitzer, Florian; Cossairt, Oliver (2021 IEEE International Conference on Image Processing (ICIP), 2021)
The utilization of computational photography becomes increasingly essential in the medical field. Today, imaging techniques for dermatology range from two-dimensional (2D) color imagery with a mobile device to professional clinical imaging systems measuring additional detailed three-dimensional (3D) data. The latter are commonly expensive and not accessible to a broad audience. In this work, we propose a novel system and software framework that relies only on low-cost (and even mobile) commodity devices present in every household to measure detailed 3D information of the human skin with a 3D-gradient-illumination-based method. We believe that our system has great potential for early-stage diagnosis and monitoring of skin diseases, especially in vastly populated or underdeveloped areas.<br
Keywords: Computational imaging; Topographic imaging; 3D imaging; Dermatology

SlicerOCT: A 3-D visualization platform for orthoplane viewing of OCT(A) datasets - Husvogt, Lennart; Moult, Eric M.; Lee, Byungkun; Waheed, Nadia K.; Hornegger, Joachim; Spaide, Richard F.; Maier, Andreas; Fujimoto, James G. (Investigative Ophthalmology & Visual Science, 2016)
Purpose : 3D Slicer is a visualization platform for radiographic data (computed tomography, magnetic resonance imaging, ultrasound). The purpose of this study is to extend 3D Slicer to enable simultaneous, orthoplane viewing of volumetric optical coherence tomography (OCT) and OCT angiography (OCTA) data. Methods : We propose an OCT specific module, “SlicerOCT,” which leverages 3D Slicer’s existing capabilities for orthoplane viewing and volume rendering. OCT and OCTA data are stored in different layers, which allows simultaneous display; layers can be toggled on and off. OCT specific functions, such as volume projection and OCTA thresholding, are available. Orthoplane views can be scrolled through in a manner analogous to a conventional radiology viewer and data can be rendered volumetrically. Results : Figure 1 shows a SlicerOCT display of OCT and OCTA data from a patient with exudative age-related macular degeneration (AMD). The choroidal neovascularization (CNV) is clearly visible in the OCT angiograms and corresponding structural en face displays (right panels), while CNV location under the retinal pigment epithelium (RPE) and intraretinal cysts are visible in the OCT B-scan display (bottom left). Figure 2 shows a SlicerOCT display of OCT and OCTA data from a patient with geographic atrophy (GA). Areas of RPE alteration are visible on OCTA, while areas of choriocapillaris loss are seen on OCTA. Unlike traditional en face visualization of OCTA data, orthoplane viewing enables interpretation of individual OCTA B-scans, which can help identify artifacts, such as incorrect segmentation, signal attenuation, and decorrelation tails. Conclusions : SlicerOCT enables simultaneous, orthoplane visualization of OCT and OCTA datasets, which promises to enable more accurate interpretation of OCTA data and is especially crucial for studying diseases whose progression alters both structure and blood flo
UnivIS-Import:2018-09-06:Pub.2016.tech.IMMD.IMMD5.slicer
Keywords: oct; octa; oct angiography; 3d slicer

SlideRunner - A Tool for Massive Cell Annotations in Whole Slide Images - Aubreville, Marc; Bertram, Christof A.; Klopfleisch, Robert; Maier, Andreas (Bildverarbeitung für die Medizin 2018 - Algorithmen - Systeme - Anwendungen. Proceedings des Workshops vom 11. bis 13. März 2018 in Erlangen, 2018)
Large-scale image data such as digital whole-slide histology images pose a challenging task at annotation software solutions. Today, a number of good solutions with varying scopes exist. For cell annotation, however, we find that many do not match the prerequisites for fast annotations. Especially in the field of mitosis detection, it is assumed that detection accuracy could significantly benefit from larger annotation databases that are currently however very troublesome to produce. Further, multiple independent (blind) expert labels are a big asset for such databases, yet there is currently no tool for this kind of annotation available. To ease this tedious process of expert annotation and grading, we introduce SlideRunner, an open source annotation and visualization tool for digital histopathology, developed in close cooperation with two pathologists. SlideRunner is capable of setting annotations like object centers (for e.g. cells) as well as object boundaries (e.g. for tumor outlines). It provides single-click annotations as well as a blind mode for multi-annotations, where the expert is directly shown the microscopy image containing the cells that he has not yet rated.<br
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.slider

SliTraNet: Automatic Detection of Slide Transitions in Lecture Videos using Convolutional Neural Networks - Sindel, Aline; Hernandez, Abner; Yang, Seung Hee; Christlein, Vincent; Maier, Andreas (Proceedings of the OAGM Workshop 2021. Computer Vision and Pattern Analysis Across Domains, 2022)
With the increasing number of online learning material in the web, search for specific content in lecture videos can be time consuming. Therefore, automatic slide extraction from the lecture videos can be helpful to give a brief overview of the main content and to support the students in their studies. For this task, we propose a deep learning method to detect slide transitions in lectures videos. We first process each frame of the video by a heuristic-based approach using a 2-D convolutional neural network to predict transition candidates. Then, we increase the complexity by employing two 3-D convolutional neural networks to refine the transition candidates. Evaluation results demonstrate the effectiveness of our method in finding slide transitions.<br

SmartPatch: Improving Handwritten Word Imitation with Patch Discriminators - Mattick, Alexander; Mayr, Martin; Seuret, Mathias; Maier, Andreas; Christlein, Vincent (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2021)
As of recent generative adversarial networks have allowed for big leaps in the realism of generated images in diverse domains, not the least of which being handwritten text generation. The generation of realistic-looking handwritten text is important because it can be used for data augmentation in handwritten text recognition (HTR) systems or human-computer interaction. We propose SmartPatch, a new technique increasing the performance of current state-of-the-art methods by augmenting the training feedback with a tailored solution to mitigate pen-level artifacts. We combine the well-known patch loss with information gathered from the parallel trained handwritten text recognition system and the separate characters of the word. This leads to a more enhanced local discriminator and results in more realistic and higher-quality generated handwritten words.
CRIS-Team Scopus Importer:2021-10-01
Keywords: Generative adversarial networks; Offline handwriting generation; Patch discriminator

Smoothed and Resolved Thresholding (SmaRT-) Display: A New OCTA Display Technique to Resolve the Low Flow Ambiguity - Yiu, Patrick; Moult, Eric M.; Ploner, Stefan; Lee, Byungkun; Husvogt, Lennart; Maier, Andreas; Spaide, Richard F.; Duker, Jay S.; Waheed, Nadia K.; Fujimoto, James G. (Investigative Ophthalmology & Visual Science, 2017)
Purpose : OCT angiography (OCTA) provides clinicians with a new perspective on ocular vasculature. However, OCTA is fundamentally different from OCT, and its complex processing steps generate new artifacts that are not present in OCT. The “thresholding” step in OCTA, which removes (makes black) regions of low OCT signal, results in an ambiguity between areas of low OCT signal and areas of low blood flow. In this study we present a new OCTA display technique, SmaRT-Display OCTA, which obviates this ambiguity. Methods : We propose a mapping scheme based on the hue-saturation-value (HSV) colorspace wherein the OCT signal is mapped to a sigmoidal curve in the value coordinate, the unthresholded OCTA signal is mapped to the saturation coordinate, and the hue coordinate is fixed at red. Results : SmaRT-Display removes low flow ambiguities in both cross-sectional and en face views. Conclusions : SmaRT-Display has the potential to reduce misinterpretation of OCTA images and is a first step in expanding the standards of how OCTA data can be presented. This is an abstract that was submitted for the 2017 ARVO Annual Meeting, held in Baltimore, MD, May 7-11, 201
UnivIS-Import:2018-09-06:Pub.2017.tech.IMMD.IMMD5.smooth
Keywords: oct; octa; oct angiography; angiography thresholding

Smooth Ride: Low-Pass Filtering of Manual Segmentations Improves Consensus - Maier, Jennifer; Black, Marianne; Hall, Mary; Choi, Jang-Hwan; Levenston, Marc; Gold, Garry; Fahrig, Rebecca; Eskofier, Björn; Maier, Andreas (Bildverarbeitung für die Medizin 2019, 2019)

SniffyArt: The Dataset of Smelling Persons - Zinnen, Mathias; Hussian, Azhar; Tran, Thi My Hang; Madhu, Prathmesh; Maier, Andreas; Christlein, Vincent (Proceedings of the 5th Workshop on AnalySis, Understanding and ProMotion of HeritAge Contents, 2023)
Smell gestures play a crucial role in the investigation of past smells in the visual arts yet their automated recognition poses significant challenges. This paper introduces the SniffyArt dataset, consisting of 1941 individuals represented in 441 historical artworks. Each person is annotated with a tightly fitting bounding box, 17 pose keypoints, and a gesture label. By integrating these annotations, the dataset enables the development of hybrid classification approaches for smell gesture recognition. The dataset's high-quality human pose estimation keypoints are achieved through the merging of five separate sets of keypoint annotations per person. The paper also presents a baseline analysis, evaluating the performance of representative algorithms for detection, keypoint estimation, and classification tasks, showcasing the potential of combining keypoint estimation with smell gesture classification. The SniffyArt dataset lays a solid foundation for future research and the exploration of multi-task approaches leveraging pose keypoints and person boxes to advance human gesture and olfactory dimension analysis in historical artworks.<br

Some Investigations on Robustness of Deep Learning in Limited Angle Tomography - Huang, Yixing; Würfl, Tobias; Breininger, Katharina; Liu, Ling; Lauritsch, Günter; Maier, Andreas (Proceedings of the 21st International Conference on Medical Image Computing & Computer Assisted Intervention, 2018)
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.somein
Keywords: Deep learning; limited angle tomography; robustness; adversarial example

Source-detector trajectory optimization in cone-beam computed tomography: a comprehensive review on today's state-of-the-art - Hatamikia, S.; Biguri, Ander; Herl, G.; Kronreif, G.; Reynolds, T.; Kettenbach, J.; Russ, T.; Tersol, A.; Maier, Andreas; Figl, M.; Siewerdsen, J. H.; Birkfellner, W. (Physics in Medicine and Biology, 2022)
Cone-beam computed tomography (CBCT) imaging is becoming increasingly important for a wide range of applications such as image-guided surgery, image-guided radiation therapy as well as diagnostic imaging such as breast and orthopaedic imaging. The potential benefits of non-circular source-detector trajectories was recognized in early work to improve the completeness of CBCT sampling and extend the field of view (FOV). Another important feature of interventional imaging is that prior knowledge of patient anatomy such as a preoperative CBCT or prior CT is commonly available. This provides the opportunity to integrate such prior information into the image acquisition process by customized CBCT source-detector trajectories. Such customized trajectories can be designed in order to optimize task-specific imaging performance, providing intervention or patient-specific imaging settings. The recently developed robotic CBCT C-arms as well as novel multi-source CBCT imaging systems with additional degrees of freedom provide the possibility to largely expand the scanning geometries beyond the conventional circular source-detector trajectory. This recent development has inspired the research community to innovate enhanced image quality by modifying image geometry, as opposed to hardware or algorithms. The recently proposed techniques in this field facilitate image quality improvement, FOV extension, radiation dose reduction, metal artifact reduction as well as 3D imaging under kinematic constraints. Because of the great practical value and the increasing importance of CBCT imaging in image-guided therapy for clinical and preclinical applications as well as in industry, this paper focuses on the review and discussion of the available literature in the CBCT trajectory optimization field. To the best of our knowledge, this paper is the first study that provides an exhaustive literature review regarding customized CBCT algorithms and tries to update the community with the clarification of in-depth information on the current progress and future trends.
CRIS-Team WoS Importer:2022-08-26

Spatial Distribution of Choriocapillaris Impairment in Eyes with Choroidal Neovascularization Secondary to Age-Related Macular Degeneration - Moult, Eric M.; Alibhai, A. Yasin; Rebhun, Carl B.; Lee, Byungkun; Ploner, Stefan; Schottenhamml, Julia; Husvogt, Lennart; Baumal, Caroline R.; Witkin, André J.; Maier, Andreas; Duker, Jay S.; Rosenfeld, Philip; et al. (Retina-The Journal of Retinal and Vitreous Diseases, 2019)
Purpose: To develop an optical coherence tomography angiography (OCTA)-based framework for quantitatively analyzing the spatial distribution of choriocapillaris (CC) impairment around choroidal neovascularization (CNV) secondary to age-related macular degeneration. Methods: In a retrospective, cross-sectional study, 400-kHz swept-source OCTA images from 7 eyes of 6 patients with CNV secondary to age-related macular degeneration were quantitatively analyzed using custom software. A lesion-centered zonal OCTA analysis technique—which portioned the field-of-view into zones relative to CNV boundaries—was developed to quantify the spatial dependence of CC flow deficits. Results: Quantitative, lesion-centered zonal analysis of CC OCTA images revealed highest flow-deficit percentages near CNV boundaries, decreasing in zones farther from the boundaries. Optical coherence tomography angiography using shorter (1.5 ms) interscan times revealed more severe flow deficits than OCTA using longer (3.0 ms) interscan times; however, spatial trends were similar for both interscan times. A detailed description of the OCTA processing steps and parameters was provided so as to elucidate their influence on quantitative measurements. Conclusion: Impairment of the CC, assessed by flow-deficit percentages, was most prominent closest to CNV boundaries. The lesion-centered zonal analysis technique enabled quantitative CC measurements relative to focal lesions. Understanding how processing steps, imaging/processing parameters, and artifacts can affect quantitative CC measurements is important for longitudinal, OCTA-based studies of disease progression, and treatment respons

Spatial Lesion Graphs: Analyzing Liver Metastases with Geometric Deep Learning for Cancer Survival Regression - Rist, Leonhard; Taubmann, Oliver; Muhlberg, Alexander; Denzinger, Felix; Thamm, Florian; Sühling, Michael; Norenberg, Dominik; Holch, Julian Walter; Maurus, Stefan; Gebauer, Leonie; Huber, Thomas; Maier, Andreas (Proceedings - International Symposium on Biomedical Imaging, 2023)
The degree of metastatic progress is a crucial factor in cancer assessment and is often estimated from few target lesions. However, features of the overall configuration of metastases, such as spread distance in the liver, offer further predictive information. Graphs can represent both local characteristics of individual lesions and their global spatial distribution. In this work, liver metastases stemming from a primary pancreatic tumor are transformed patient-wise into spatial lesion graphs and processed by geometric deep learning to estimate hazard ratios. The influence of lesion features and graph topologies is investigated in a data set of 78 patients to provide model design guidelines. Graph attention and differential pooling networks were selected as architectures to capitalize on the proposed topologies and provide meaningful visualizations of the decision process. Compared with conventional survival analysis methods and deep learning models, both networks show improvement with a maximum concordance of 68.9 %.
Created from Fastlane, Scopus look-up
Keywords: cancer; Geometric deep learning; graphs; liver; metastasis; multi-focal diseases; survival analysis

Spatial-temporal total variation regularization (STTVR) for 4D-CT reconstruction - Wu, Haibo; Maier, Andreas; Fahrig, Rebecca; Hornegger, Joachim (Medical Imaging 2012: Physics of Medical Imaging, 2012)
Four dimensional computed tomography (4D-CT) is very important for treatment planning in thorax or abdomen area, e.g. for guiding radiation therapy planning. The respiratory motion makes the reconstruction problem illposed. Recently, compressed sensing theory was introduced. It uses sparsity as a prior to solve the problem and improves image quality considerably. However, the images at each phase are reconstructed individually. The correlations between neighboring phases are not considered in the reconstruction process. In this paper, we propose the spatial-temporal total variation regularization (STTVR) method which not only employs the sparsity in the spatial domain but also in the temporal domain. The algorithm is validated with XCAT thorax phantom. The Euclidean norm of the reconstructed image and ground truth is calculated for evaluation. The results indicate that our method improves the reconstruction quality by more than 50% compared to standard ART. © 2012 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).

Spatio-Temporal Handwriting Imitation - Mayr, Martin; Stumpf, Martin; Nicolaou, Anguelos; Seuret, Mathias; Maier, Andreas; Christlein, Vincent (European Conference on Computer Vision, 2020)
Most people think that their handwriting is unique and cannot be imitated by machines, especially not using completely new content. Current cursive handwriting synthesis is visually limited or needs user interaction. We show that subdividing the process into smaller subtasks makes it possible to imitate someone’s handwriting with a high chance to be visually indistinguishable for humans. Therefore, a given handwritten sample will be used as the target style. This sample is transferred to an online sequence. Then, a method for online handwriting synthesis is used to produce a new realistic-looking text primed with the online input sequence. This new text is rendered and style-adapted to the input pen. We show the effectiveness of the pipeline by generating in- and out-of- vocabulary handwritten samples that are validated in a comprehensive user study. Additionally, we show that also a typical writer identification system can partially be fooled by the created fake handwritings.<br
Keywords: Offline Handwriting Generation; Style Transfer; Forgery; Handwriting Synthesis

Spatio-temporally Regularized 4-D Cardiovascular C-arm CT Reconstruction Using a Proximal Algorithm - Taubmann, Oliver; Unberath, Mathias; Lauritsch, Günter; Achenbach, Stephan; Maier, Andreas (Proceedings of the 2017 IEEE International Symposium on Biomedical Imaging, 2017)
Tomographic reconstruction of cardiovascular structures from rotational angiograms acquired with interventional C-arm devices is challenging due to cardiac motion. Gating strategies are widely used to reduce data inconsistency but come at the cost of angular undersampling. We employ a spatio-temporally regularized 4-D reconstruction model, which is solved using a proximal algorithm, to handle the substantial undersampling associated with a strict gating setup. In a numerical phantom study based on the CAVAREV framework, similarity to the ground truth is improved from 82.3% to 87.6%by this approach compared to a state-of-the-art motion compensation algorithm, whereas previous regularized methods evaluated on this phantom achieved results below 80%. We also show first image results for a clinical patient data set.
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.spatio_6
Keywords: C-arm CT; Cardiovascular reconstruction; Spatio-temporal regularization; Total variation

Spatio-temporal Multi-task Learning for Cardiac MRI Left Ventricle Quantification - Vesal, Sulaiman; Gu, Mingxuan; Maier, Andreas; Ravikumar, Nishant (IEEE Journal of Biomedical and Health Informatics, 2020)
Quantitative assessment of cardiac left ventricle (LV) morphology is essential to assess cardiac function and improve the diagnosis of different cardiovascular diseases. In current clinical practice, LV quantification depends on the measurement of myocardial shape indices, which is usually achieved by manual contouring of the endo- and epicardial. However, this process subjected to inter and intra-observer variability, and it is a time-consuming and tedious task. In this paper, we propose a spatio-temporal multi-task learning approach to obtain a complete set of measurements quantifying cardiac LV morphology, regional-wall thickness (RWT), and additionally detecting the cardiac phase cycle (systole and diastole) for a given 3D Cine-magnetic resonance (MR) image sequence. We first segment cardiac LVs using an encoder-decoder network and then introduce a multitask framework to regress 11 LV indices and classify the cardiac phase, as parallel tasks during model optimization. The proposed deep learning model is based on the 3D spatio-temporal convolutions, which extract spatial and temporal features from MR images. We demonstrate the efficacy of the proposed method using cine-MR sequences of 145 subjects and comparing the performance with other state-of-the-art quantification methods. The proposed method obtained high prediction accuracy, with an average mean absolute error (MAE) of 129 mm2, 1.23 mm, 1.76 mm, Pearson correlation coefficient (PCC) of 96.4%, 87.2%, and 97.5% for LV and myocardium (Myo) cavity regions, 6 RWTs, 3 LV dimensions, and an error rate of 9.0% for phase classification. The experimental results highlight the robustness of the proposed method, despite varying degrees of cardiac morphology, image appearance, and low contrast in the cardiac MR sequences
Keywords: Left Ventricle Quantification; Cardiac MRI; Cardiac Segmentation; Deep Learning; Myocardial Infraction

Speaking Clearly, Understanding Better: Predicting the L2 Narrative Comprehension of Chinese Bilingual Kindergarten Children Based on Speech Intelligibility Using a Machine Learning Approach - Hung, Hiuching; Pérez Toro, Paula Andrea; Arias Vergara, Tomás; Maier, Andreas; Nöth, Elmar (Interspeech 2023, 2023)
This study investigated the relationship of speech intelligibility and the narrative comprehension among bilingual kindergarten children and how well the speech intelligibility of second language (L2) predicted the L2 narrative comprehension using a machine learning approach. Fifty Chinese-English bilingual children aged 5-6 years old participated in this study by taking a narrative comprehension test. Their L2 narrative comprehension was assessed using the MAIN test. The speech intelligibility was assessed in terms of twenty-four features that encode confidence levels with respect to phoneme and word classifiers trained on native speaker speech data. Our hypothesis posits that it is possible to predict L2 narrative comprehension based on speech intelligibility features. By using seven out of the twenty-four considered features we were able to make predictions of the MAIN test scores with an RMSE of 2.13 and a Pearson correlation coefficient of 0.468 based on a data set of 50 bilingual kindergarten children. We conclude the paper by providing pedagogical implications for second language teaching as well as suggestions for future work.<br
CRIS-Team Scopus Importer:2023-09-29
Keywords: bilingual kindergarten children; computational paralinguistics; human-computer interaction; L2 narrative comprehension; speech intelligibility; speech recognition

Spectrum Binning Approach for Multi-Material Beam Hardening Correction (MMBHC) in CT - Yang, Qiao; Wu, Meng; Maass, Nicole; Maier, Andreas; Hornegger, Joachim; Fahrig, Rebecca (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.spectr_5

Speech Classification for Sigmatism in Children - Botinhao, Cassia; Nöth, Elmar; Hornegger, Joachim; Maier, Andreas (Proceedings of the 5th Russian-Bavarian Conference on Biomedical Engineering, 2009)

Speech intelligibility in patients with oral cancer: An objective baseline evaluation of pretreatment function and impairment - Stelzle, Florian; Oetter, Nicolai; Göllner, Luisa; Adler, Werner; Rohde, Maximilian; Maier, Andreas; Matthies, Levi; Kesting, Marco Rainer; Knipfer, Christian (Head and Neck-Journal For the Sciences and Specialties of the Head and Neck, 2019)
Background This study seeks to identify those factors that influence the pre-therapeutic speech intelligibility in patients with oral squamous cell carcinoma (OSCC).
CRIS-Team WoS Importer:2019-04-04

Speech of Children with Cleft Lip and Palate: Automatic Assessment - Maier, Andreas (Logos, 2009)

Speech Recognizer Adaptation - Maier, Andreas (VDM Verlag Dr. Müller, 2008)

Speed-of-Sound Mapping for Pulse-Echo Ultrasound Raw Data Using Linked-Autoencoders - Khun Jush, Farnaz; Dueppenbecker, Peter M.; Maier, Andreas (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2024)
Recent studies showed the possibility of extracting SoS information from pulse-echo ultrasound raw data (a.k.a. RF data) using deep neural networks that are fully trained on simulated data. These methods take sensor domain data, i.e., RF data, as input and train a network in an end-to-end fashion to learn the implicit mapping between the RF data domain and the SoS domain. However, such networks are prone to overfitting to simulated data which results in poor performance and instability when tested on measured data. We propose a novel method for SoS mapping employing learned representations from two linked autoencoders. We test our approach on simulated and measured data acquired from human breast mimicking phantoms. We show that SoS mapping is possible using the learned representations by linked autoencoders. The proposed method has a Mean Absolute Percentage Error (MAPE) of 2.39 % on the simulated data. On the measured data, the predictions of the proposed method are close to the expected values (MAPE of 1.1 % ). Compared to an end-to-end trained network, the proposed method shows higher stability and reproducibility.
CRIS-Team Scopus Importer:2023-12-15
Keywords: Convolutional Autoencoder; Representation Learning; Speed-of-sound Mapping

Sprachverständlichkeit und Krankheitsverarbeitung nach der Therapie von Mundhöhlenkarzinomen - Sambale, Maria; Schuster, Maria; Bocklet, Tobias; Maier, Andreas; Eysholdt, Ulrich; Ströbele, Anika; Stelzle, Florian (Laryngo-Rhino-Otologie, 2010)

Sprechqualität und psychische Beeinträchtigung nach der Therapie von Mundhöhlentumoren - Böhm, Nico; Knipfer, Christian; Maier, Andreas; Bocklet, Tobias; Rohde, Maximilian; Neukam, Friedrich Wilhelm; Stelzle, Florian; Schuster, Maria (Laryngo-Rhino-Otologie, 2016)
UnivIS-Import:2016-06-01:Pub.2016.tech.IMMD.IMMD5.sprech
Keywords: Speech Intelligibility; Speech recognition

Stereo Rectification for X-ray Data Consistency Conditions - Aichert, André; Jérôme, Lesaint; Würfl, Tobias; Clackdoyle, Rolf; Desbat, Laurent; 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.stereo

Stimmbezogene und allgemeine gesundheitsbezogene Lebensqualität kehlkopfteilresezierter Patienten - Lindl, Moritz; Haderlein, Tino; Grässel, Elmar; Maier, Andreas; Ströbele, Anika; Bohr, Christopher; Schuster, Maria (Laryngo-Rhino-Otologie, 2012)

Streak Artifact Reduction in Limited Angle Tomography Using Machine Learning - Huang, Yixing; Lu, Yanye; Taubmann, Oliver; Lauritsch, Günter; Maier, Andreas (3rd Conference on Image-Guided Interventions & Fokus Neuroradiologie, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.streak

Sub-volume motion detection to speed up image-based navigators and prospective motion correction - Jäger, Anja; Beck, Thomas; Maier, Andreas (24th Annual Meeting of the International Society for Magnetic Resonance in Medicine, 2016)
UnivIS-Import:2017-01-09:Pub.2016.tech.IMMD.IMMD5.subvol
Keywords: motion compensation; magnetic resonance imaging

Superpixel Pre-segmentation of HER2 Slides for Efficient Annotation - Öttl, Mathias; Mönius, Jana; Marzahl, Christian; Rübner, Matthias; Geppert, Carol-Immanuel; Hartmann, Arndt; Beckmann, Matthias; Fasching, Peter; Maier, Andreas; Erber, Ramona; Breininger, Katharina (Informatik aktuell, 2022)
Supervised deep learning has shown state-of-the-art performance for medical image segmentation across different applications, including histopathology and cancer research; however, the manual annotation of such data is extremely laborious. In this work, we explore the use of superpixel approaches to compute a pre-segmentation of HER2 stained images for breast cancer diagnosis that facilitates faster manual annotation and correction in a second step. Four methods are compared: standard simple linear iterative clustering (SLIC) as a baseline, a domain adapted SLIC, and superpixels based on feature embeddings of a pretrained ResNet-50 and a denoising autoencoder. To tackle oversegmentation, we propose to hierarchically merge superpixels, based on their content in the respective feature space. When evaluating the approaches on fully manually annotated images, we observe that the autoencoder-based superpixels achieve a 23% increase in boundary F1 score compared to the baseline SLIC superpixels. Furthermore, the boundary F1 score increases by 73% when hierarchical clustering is applied on the adapted SLIC and the autoencoder-based superpixels. These evaluations show encouraging first results for a pre-segmentation for efficient manual refinement without the need for an initial set of annotated training data.<br
CRIS-Team Scopus Importer:2022-05-06

Super-Resolution for CEST MRI - Folle, Lukas; Tkotz, Katharina; Liebert, Andrzej; Gadjimuradov, Fasil; Kapsner, Lorenz; Fabian, Moritz; Bickelhaupt, Sebastian; Simon, David; Kleyer, Arnd; Krönke, Gerhard; Roemer, Frank; Zaiß, Moritz; et al. (Joint annual meeting ISMRM-ESMRMB ISMRT, 2022)
The resolution of chemical exchange saturation transfer (CEST) magnetic resonance imaging is limited by physical constraints. To visualize metabolic processes of small structures using CEST in patients knees, an increased resolution is necessary. In this work, we compared trilinear interpolation and zero-filling to neural network-based approaches to estimate a high-resolution image given the corresponding low-resolution data. We could show that a substantial quantitative improvement using neural networks could be achieved for unsaturated images while maintaining a comparable CEST contrast. Generalization of the method to brain CEST MRI was achieved without retraining of the networ
Keywords: MRI; Super-resolution

Super-Resolved Retinal Image Mosaicing - Köhler, Thomas; Heinrich, Axel; Maier, Andreas; Hornegger, Joachim; Tornow, Ralf-Peter (2016 IEEE 13th International Symposium on Biomedical Imaging, 2016)
The acquisition of high-resolution retinal fundus images with a large field of view (FOV) is challenging due to technological, physiological and economic reasons. This paper proposes a fully automatic framework to reconstruct retinal images of high spatial resolution and increased FOV from multiple low-resolution images captured with non-mydriatic, mobile and video-capable but low-cost cameras. Within the scope of one examination, we scan different regions on the retina by exploiting eye motion conducted by a patient guidance. Appropriate views for our mosaicing method are selected based on optic disk tracking to trace eye movements. For each view, one super-resolved image is reconstructed by fusion of multiple video frames. Finally, all super-resolved views are registered to a common reference using a novel polynomial registration scheme and combined by means of image mosaicing. We evaluated our framework for a mobile and low-cost video fundus camera. In our experiments, we reconstructed retinal images of up to 30° FOV from 10 complementary views of 15° FOV. An evaluation of the mosaics by human experts as well as a quantitative comparison to conventional color fundus images encourage the clinical usability of our framework.
UnivIS-Import:2017-11-07:Pub.2016.tech.IMMD.IMMD5.superr_1
Keywords: eye tracking; fundus video imaging; mosaicing; Retinal imaging; super-resolution

Supervised Contrastive Learning for Robust and Efficient Multi-modal Emotion and Sentiment Analysis - Gomaa, Ahmed; Maier, Andreas; Kosti, Ronak Vijaypal (2022 26TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR), 2022)
CRIS-Team WoS Importer:2023-02-17

Surface Registration to Estimate Motion in CBCT - Bier, Bastian; Unberath, Mathias; Ravikumar, Nishant; Maier, Jennifer; Gooya, Ali; Taylor, Zeike A.; Frangi, Alejandro F.; Gold, Garry; Fahrig, Rebecca; Maier, Andreas (2017 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC), 2017)
UnivIS-Import:2018-09-11:Pub.2017.tech.IMMD.IMMD5.surfac

Surrogate-Driven Estimation of Respiratory Motion and Layers in X-Ray Fluoroscopy - Fischer, Peter; Pohl, Thomas; Maier, Andreas; Hornegger, Joachim (Medical Image Computing and Computer-Assisted Intervention MICCAI 2015, 2015)
UnivIS-Import:2015-10-26:Pub.2015.tech.IMMD.IMMD5.surrog

Symmetry, Outliers, and Geodesics in Coronary Artery Centerline Reconstruction from Rotational Angiography - Unberath, Mathias; Taubmann, Oliver; Hell, Michaela; Achenbach, Stephan; Maier, Andreas (Medical Physics, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.symmet
Keywords: angiography; cone-beam CT; postprocessing; spanning trees

Symmetry prior for epipolar consistency - Preuhs, Alexander; Maier, Andreas; Manhart, Michael; Kowarschik, Markus; Hoppe, Elisabeth; Fotouhi, Javad; Navab, Nassir; Unberath, Mathias (International Journal of Computer Assisted Radiology and Surgery, 2019)
Purpose: For a perfectly plane symmetric object, we can find two views—mirrored at the plane of symmetry—that will yield the exact same image of that object. In consequence, having one image of a plane symmetric object and a calibrated camera, we automatically have a second, virtual image of that object if the 3-D location of the symmetry plane is known. Methods: We propose a method for estimating the symmetry plane from a set of projection images as the solution of a consistency maximization based on epipolar consistency. With the known symmetry plane, we can exploit symmetry to estimate in-plane motion by introducing the X-trajectory that can be acquired with a conventional short-scan trajectory by simply tilting the acquisition plane relative to the plane of symmetry. Results: We inspect the symmetry plane estimation on a real scan of an anthropomorphic human head phantom and show the robustness using a synthetic dataset. Further, we demonstrate the advantage of the proposed method for estimating in-plane motion using the acquired projection data. Conclusion: Symmetry breakers in the human body are widely used for the detection of tumors or strokes. We provide a fast estimation of the symmetry plane, robust to outliers, by computing it directly from a set of projections. Further, by coupling the symmetry prior with epipolar consistency, we overcome inherent limitations in the estimation of in-plane motion.
CRIS-Team Scopus Importer:2019-08-06
Keywords: Cone-beam CT; Consistency conditions; Data completeness; Motion compensation; Symmetry; Tomographic reconstruction

SyNCCT: Synthetic Non-Contrast Images of the Brain from Single-Energy Computed Tomography Angiography - Thamm, Florian; Taubmann, Oliver; Denzinger, Felix; Jürgens, Markus; Ditt, Hendrik; Maier, Andreas (Medical Image Computing and Computer Assisted Intervention – MICCAI 2021, 2021)
By injecting contrast agent during a CT acquisition, the vascular system can be enhanced. This acquisition type is known as CT Angiography (CTA). However, due to typically lower dose levels of CTA scans compared to non-contrast CT acquisitions (NCCT) and the employed reconstruction designed specifically for vessel reconstruction, soft tissue contrast in the brain parenchyma is usually subpar. Hence, an NCCT scan is preferred for the visualization of such tissue. We propose SyNCCT, an approach which synthesizes NCCT images from the CTA domain by removing enhanced vessel structures and improving soft tissue contrast. Contrary to virtual non-contrast (VNC) images based on dual energy scans, which target the physically accurate removal of iodine rather than generating a realistic NCCT with improved gray/white matter separation, our approach only requires a conventional single-energy acquisition. By design, our method integrates prior domain knowledge and employs residual learning as well as a discriminator to achieve perceptual realism. In our data set of patients with ischemic stroke, the absolute differences in automatic ASPECT scoring, which rates early signs of an occlusion in the anterior circulation on a scale from 0 (most severe) to 10 (no signs), was 0.78±0.75 (median of 1) when comparing our SyNCCT to the real NCCT images. Qualitatively, realistic appearance of the images was confirmed by means of a Turing test with a radiologist, who classified 64% of 64 (32 real, 32 generated) images correctly. Two other physicians classified 65% correctly, on average.
Keywords: Computed Tomography Angiography; Domain Transfer; Single Energy CT

Synthetic fundus fluorescein angiography using deep neural networks - Schiffers, Florian; Yu, Zekuan; Arguin, Steve; Maier, Andreas; Ren, Qiushi (Informatik aktuell, 2018)
Fundus fluorescein angiography yields complementary image information when compared to conventional fundus imaging. Angiographic imaging, however, may pose risks of harm to the patient. The output from both types of imaging have different characteristics, but the most prominent features of the fundus are shared in both images. Thus, the question arises if conventional fundus images alone provide enough information to synthesize an angiographic image. Our research analyzes the capacity of deep neural networks to synthesize virtual angiographic images from their conventional fundus counterparts.
CRIS-Team Scopus Importer:2022-06-05

Synthetic Glacier SAR Image Generation from Arbitrary Masks Using Pix2Pix Algorithm - Dietrich-Sussner, Rosanna; Davari, Amirabbas; Seehaus, Thorsten; Braun, Matthias; Christlein, Vincent; Maier, Andreas; Rieß, Christian (2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021)
Supervised machine learning requires a large amount of labeled data to achieve proper test results. However, generating accurately labeled segmentation maps on remote sensing imagery, including images from synthetic aperture radar (SAR), is tedious and highly subjective. In this work, we propose to alleviate the issue of limited training data by generating synthetic SAR images with the pix2pix algorithm [1]. This algorithm uses conditional Generative Adversarial Networks (cGANs) to generate an artificial image while preserving the structure of the input. In our case, the input is a segmentation mask, from which a corresponding synthetic SAR image is generated. We present different models, perform a comparative study and demonstrate that this approach synthesizes convincing glaciers in SAR images with promising qualitative and quantitative results.
Keywords: Training; Image segmentation; Training data; Machine learning; Generative adversarial networks; Radar polarimetry; Synthetic aperture radar; image-to-image translation; conditional GAN; limited training data; synthetic data

Synthetic Image Rendering Solves Annotation Problem in Deep Learning Nanoparticle Segmentation - Mill, Leonid; Wolff, David; Gerrits, Nele; Philipp, Patrick; Kling, Lasse; Vollnhals, Florian; Ignatenko, Andrew; Jaremenko, Christian; Huang, Yixing; De Castro, Olivier; Audinot, Jean Nicolas; Nelissen, Inge; et al. (Small Methods, 2021)
Nanoparticles occur in various environments as a consequence of man-made processes, which raises concerns about their impact on the environment and human health. To allow for proper risk assessment, a precise and statistically relevant analysis of particle characteristics (such as size, shape, and composition) is required that would greatly benefit from automated image analysis procedures. While deep learning shows impressive results in object detection tasks, its applicability is limited by the amount of representative, experimentally collected and manually annotated training data. Here, an elegant, flexible, and versatile method to bypass this costly and tedious data acquisition process is presented. It shows that using a rendering software allows to generate realistic, synthetic training data to train a state-of-the art deep neural network. Using this approach, a segmentation accuracy can be derived that is comparable to man-made annotations for toxicologically relevant metal-oxide nanoparticle ensembles which were chosen as examples. The presented study paves the way toward the use of deep learning for automated, high-throughput particle detection in a variety of imaging techniques such as in microscopies and spectroscopies, for a wide range of applications, including the detection of micro- and nanoplastic particles in water and tissue samples.
CRIS-Team Scopus Importer:2021-05-14
Keywords: helium ion microscopy; image analysis; machine learning; nanoparticles; segmentation; toxicology

Systematic interpretation of confocal laser endomicroscopy: larynx and pharynx confocal imaging score Interpretazione sistematica dell’endomicroscopia confocale laser: punteggio endomicroscopico confocale laringeo e faringeo - Sievert, Matti; Mantsopoulos, Konstantinos; Müller-Hübner, Sarina; Eckstein, Markus; Rupp, Robin; Aubreville, Marc; Stelzle, Florian; Oetter, Nicolai; Maier, Andreas; Iro, Heinrich; Goncalves, Miguel (Acta Otorhinolaryngologica Italica, 2022)
Objective. Development and validation of a confocal laser endomicroscopy (CLE) classification score for the larynx and pharynx. Methods. Thirteen patients (154 video sequences, 9240 images) with laryngeal or phar-yngeal SCC were included in this prospective study between October 2020 and February 2021. Each CLE sequence was correlated with the gold standard of histopathological ex-amination. Based on a dataset of 94 video sequences (5640 images), a scoring system was developed. In the remaining 60 sequences (3600 images), the score was validated by four CLE experts and four head and neck surgeons who were not familiar with CLE. Results. Tissue homogeneity, cell size, borders and clusters, capillary loops and the nu-cleus/cytoplasm ratio were defined as the scoring criteria. Using this score, the CLE experts obtained an accuracy, sensitivity, and specificity of 90.8%, 95.1%, and 86.4%, respectively, and the CLE non-experts of 86.2%, 86.4%, and 86.1%. Interobserver agreement Fleiss’ kappa was 0.8 and 0.6, respectively. Conclusions. CLE can be reliably evaluated based on defined and reproducible imaging features, which demonstrate a high diagnostic value. CLE can be easily integrated into the intraoperative setting and generate real-time, in-vivo microscopic images to demarcate malignant changes.
CRIS-Team Scopus Importer:2022-03-25
Keywords: Classification system; Confocal laser endomicroscopy; Head and neck cancer; Larynx; Non-invasive histological imaging; Pharynx

System Theory - Fischer, Peter; Sembritzki, Klaus; Maier, Andreas (Medical Imaging Systems: An Introductory Guide, 2018)
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.system

T-2* Accelerated quantification of tissue sodium concentration in skeletal muscle tissue: quantitative capability of dictionary learning compressed sensing - Utzschneider, Matthias; Behl, Nicolas G. R.; Lachner, Sebastian; Gast, Lena; Maier, Andreas; Uder, Michael; Nagel, Armin Michael (Magnetic Resonance Materials in Physics Biology and Medicine, 2020)
OBJECTIVE: To accelerate tissue sodium concentration (TSC) quantification of skeletal muscle using 23Na MRI and 3D dictionary-learning compressed sensing (3D-DLCS). MATERIALS AND METHODS: Simulations and in vivo 23Na MRI examinations of calf muscle were performed with a nominal spatial resolution of [Formula: see text]. Fully sampled and three undersampled 23Na MRI data sets (undersampling factors (USF) = 3, 4.4, 6.7) were evaluated. Ten healthy subjects were examined on a 3 Tesla MRI system. Results of the simulation study and the in vivo measurements were compared to the ground truth (GT) and the fully sampled fast Fourier transform (NUFFT) reconstruction, respectively. RESULTS: Reconstruction results of simulated data with optimized 3D-DLCS yielded a lower deviation (< 4%) from the GT than results of the NUFFT reconstruction (> 5%) and a lower standard deviation (SD). For in vivo measurements, a TSC of [Formula: see text] was observed. The mean deviation from the reference is lower for the undersampled 3D-DLCS reconstructions (3.4%) than for NUFFT reconstructions (4.6%). SD is reduced using 3D-DLCS. Compared to a fully sampled NUFFT reconstruction, acquisition time could be reduced by a factor of 4.4 while maintaining similar quantitative accuracy. DISCUSSION: The optimized 3D-DLCS reconstruction enables accelerated TSC measurements with high quantification accuracy.
CRIS-Team WoS Importer:2020-01-31

Table Motion Detection in Interventional Coronary Angiography - Rajput, Junaid Rasool; Shetty, Karthik; Maier, Andreas; Berger, Martin (Informatik aktuell, 2021)
CRIS-Team Scopus Importer:2021-04-19

Talbot-Lau X-ray phase contrast for tiling-based acquisitions without reference scanning - Käppler, Sebastian; Seifert, Maria; Horn, Florian; Pelzer, Georg; Rieger, Jens; Michel, Thilo; Maier, Andreas; Anton, Gisela; Riess, Christian (Medical Physics, 2017)
Keywords: grating interferometer; reference image; phase-contrast; x-ray imaging

Tapping the Potential of Earth Observation - Calving Front Detection in SAR Images using Deep Learning Techniques - Gourmelon, Nora; Seehaus, Thorsten; Davari, Amirabbas; Braun, Matthias; Maier, Andreas; Christlein, Vincent (EGU General Assembly 2021, 2021)
The calving fronts of lake or marine terminating glaciers provide information about the state of glaciers. A change in its position can affect the flow of the entire glacier system, and the loss of ice mass as icebergs calve-off and discharge into the ocean has a multi-scale impact on the global hydrological cycle. The calving fronts can be manually delineated in Synthetic Aperture Radar (SAR) images. However, this is a time-consuming, tedious and expensive task. As deep learning approaches have achieved tremendous success in various disciplines, such as medical image processing and computer vision, the project Tapping the Potential of Earth Observation (TAPE) is amongst other things dedicated to applying deep learning techniques to calving front detection. So far, all our experiments have employed U-Net based architectures, as the U-Net is state-of-the-art in semantic image segmentation. A major challenge of front detection is the class imbalance: The front has significantly fewer pixels than the remaining parts of the SAR image. Hence, we developed variants of the U-Net specifically addressing this challenge including an Attention U-Net, a probabilistic Bayesian U-Net, as well as a U-Net with a distance map-based binary cross-entropy (BCE) loss function and a Mathews correlation coefficient (MCC) as early stopping criterion. In future work, we plan to investigate multi-task learning and a segmentation of the SAR image into different classes (i.e. ocean, glacier and rocks) to enhance the quality and efficiency of the front detection.<br

Task-based Generation of Optimized Projection Sets using Differentiable Ranking - Schneider, Linda-Sophie; Thies, Mareike; Syben, Christopher; Schielein, Richard; Unberath, Mathias; Maier, Andreas (Fully3D 2023 Proceedings, 2023)
We present a method for selecting valuable projections in computed tomography (CT) scans to enhance image reconstruction and diagnosis. The approach integrates two important factors, projection-based detectability and data completeness, into a single feed-forward neural network. The network evaluates the value of projections, processes them through a differentiable ranking function and makes the final selection using a straight-through estimator. Data completeness is ensured through the label provided during training. The approach eliminates the need for heuristically enforcing data completeness, which may exclude valuable projections. The method is evaluated on simulated data in a non-destructive testing scenario, where the aim is to maximize the reconstruction quality within a specified region of interest. We achieve comparable results to previous methods, laying the foundation for using reconstruction-based loss functions to learn the selection of projections.
Keywords: Computed Tomography (CT); Deep Learning; CT Trajectory Optimization

Task-Specific Trajectory Optimisation for Twin-Robotic X-Ray Tomography - Herl, Gabriel; Hiller, Jochen; Thies, Mareike; Zaech, Jan-Nico; Unberath, Mathias; Maier, Andreas (IEEE Transactions on Computational Imaging, 2021)
With the advent of robotic C-arm computed tomography (CT) systems in medicine and twin-robotic CT systems in industry, new possibilities for the realisation of complex trajectories for CT scans are emerging. These trajectories will increase the range of CT applications, enable optimisation of image quality for many applications and open up new possibilities to reduce scan time and radiation dose. In this work, trajectory optimisation methods for optimising both, task-based data quality and data completeness, are presented by combining two different metrics. On the one hand, task-based data quality is optimised with a proven observer model. On the other hand, a Tuy-based metric is utilised to optimise data completeness. Both metrics capture mutually exclusive properties of the trajectory which are necessary, but alone are not sufficient for trajectory optimisation. Hence, existing task-driven trajectory optimisation approaches require additional input to decide on an overall optimal trajectory, e.g. in most cases constraints on the trajectory. Advantages and disadvantages of the presented methods are investigated. It is shown that by combining both metrics, trajectory optimisation for arbitrary geometries becomes possible. In application examples it is shown that this can be used for trajectory optimisation of challenging scanning tasks involving metal parts as well as for trajectory optimisation to reduce the number of projections while ensuring task-dependently high image quality. In total, the results of this work enable new applications for X-ray CT, especially for twin-robotic CT systems which are able to benefit from a high number of degrees of freedom.
Keywords: Trajectory; Computed tomography; Optimization; Robots; Geometry; Task analysis; X-ray imaging

Technical Note: PYRO-NN: Python reconstruction operators in neural networks - Syben-Leisner, Christopher; Michen, Markus; Stimpel, Bernhard; Seitz, Stephan; Ploner, Stefan; Maier, Andreas (Medical Physics, 2019)
Purpose: Recently, several attempts were conducted to transfer deep learning to medical image reconstruction. An increasingly number of publications follow the concept of embedding the computed tomography (CT) reconstruction as a known operator into a neural network. However, most of the approaches presented lack an efficient CT reconstruction framework fully integrated into deep learning environments. As a result, many approaches use workarounds for mathematically unambiguously solvable problems. Methods: PYRO-NN is a generalized framework to embed known operators into the prevalent deep learning framework Tensorflow. The current status includes state-of-the-art parallel-, fan-, and cone-beam projectors, and back-projectors accelerated with CUDA provided as Tensorflow layers. On top, the framework provides a high-level Python API to conduct FBP and iterative reconstruction experiments with data from real CT systems. Results: The framework provides all necessary algorithms and tools to design end-to-end neural network pipelines with integrated CT reconstruction algorithms. The high-level Python API allows a simple use of the layers as known from Tensorflow. All algorithms and tools are referenced to a scientific publication and are compared to existing non-deep learning reconstruction frameworks. To demonstrate the capabilities of the layers, the framework comes with baseline experiments, which are described in the supplementary material. The framework is available as open-source software under the Apache 2.0 licence at https://github.com/csyben/PYRO-NN. Conclusions: PYRO-NN comes with the prevalent deep learning framework Tensorflow and allows to setup end-to-end trainable neural networks in the medical image reconstruction context. We believe that the framework will be a step toward reproducible research and give the medical physics community a toolkit to elevate medical image reconstruction with new deep learning techniques.
CRIS-Team Scopus Importer:2019-10-22
Keywords: inverse problems; known operator learning; machine learning; open source; reconstruction

Technical Tools for the Analysis of High Medieval Papal Charters - Christlein, Vincent; Gropp, Martin; Maier, Andreas (Papstgeschichte im Digitalen Zeitalter. Neue Zugangsweisen zu einer Kulturgeschichte Europas, 2018)
UnivIS-Import:2018-02-22:Pub.2018.tech.IMMD.IMMD5.techni_0

Temporal and spatial detection of the onset of local necking and assessment of its growth behavior - Jaremenko, Christian; Affronti, Emanuela; Merklein, Marion; Maier, Andreas (Materials, 2020)
This study proposes a method for the temporal and spatial determination of the onset of local necking determined by means of a Nakajima test set-up for a DC04 deep drawing and a DP800 dual-phase steel, as well as an AA6014 aluminum alloy. Furthermore, the focus lies on the observation of the progress of the necking area and its transformation throughout the remainder of the forming process. The strain behavior is learned by a machine learning approach on the basis of the images when the process is close to material failure. These learned failure characteristics are transferred to new forming sequences, so that critical areas indicating material failure can be identified at an early stage, and consequently enable the determination of the beginning of necking and the analysis of the necking area. This improves understanding of the necking behavior and facilitates the determination of the evaluation area for strain paths. The growth behavior and traceability of the necking area is objectified by the proposed weakly supervised machine learning approach, thereby rendering a heuristic-based determination unnecessary. Furthermore, a simultaneous evaluation on image and pixel scale is provided that enables a distinct selection of the failure quantile of the probabilistic forming limit curve.
CRIS-Team Scopus Importer:2020-07-17
Keywords: Artificial intelligence; Classification; Deep learning; Forming limit curve; Machine learning; Pattern recognition; Segmentation; Sheet metal forming

Temporal and Volumetric Denoising via Quantile Sparse Image Prior - Schirrmacher, Franziska; Köhler, Thomas; Endres, Jürgen; Lindenberger, Tobias; Husvogt, Lennart; Fujimoto, James G.; Hornegger, Joachim; Dörfler, Arnd; Maier, Andreas; Hölter, Philip (Medical Image Analysis, 2018)
This paper introduces an universal and structure-preserving regularization term, called quantile sparse image (QuaSI) prior. The prior is suitable for denoising images from various medical imaging modalities. We demonstrate its effectiveness on volumetric optical coherence tomography (OCT) and computed tomography (CT) data, which show different noise and image characteristics. OCT offers high-resolution scans of the human retina but is inherently impaired by speckle noise. CT on the other hand has a lower resolution and shows high-frequency noise. For the purpose of denoising, we propose a variational framework based on the QuaSI prior and a Huber data fidelity model that can handle 3-D and 3-D+t data. Efficient optimization is facilitated through the use of an alternating direction method of multipliers (ADMM) scheme and the linearization of the quantile filter. Experiments on multiple datasets emphasize the excellent performance of the proposed method.<br
UnivIS-Import:2018-09-11:Pub.2018.tech.IMMD.IMMD5.tempor
Keywords: Spatio-temporal denoising; Variational approach; QuaSI prior; ADMM

Tenfold your photons: A physically-sound approach to filtering-based variance reduction of monte-carlo-simulated dose distributions - Roser, Philipp; Birkhold, Annette; Preuhs, Alexander; Kowarschik, Markus; Fahrig, Rebecca; Maier, Andreas (Informatik aktuell, 2020)
X-ray dose constantly gains interest in the interventional suite. With dose being generally diffcult to monitor reliably, fast computational methods are desirable. A major drawback of the gold standard based on Monte Carlo (MC) methods is its computational complexity. Besides common variance reduction techniques, filter approaches are often applied to achieve conclusive results within a fraction of time. Inspired by these methods, we propose a novel approach. We down-sample the target volume based on the fraction of mass, simulate the imaging situation, and then revert the down-sampling. To this end, the dose is weighted by the mass energy absorption, up-sampled, and distributed using a guided filter. Eventually, the weighting is inverted resulting in accurate high resolution dose distributions. The approach has the potential to considerably speed-up MC simulations since less photons and boundary checks are necessary. First experiments substantiate these assumptions. We achieve a median accuracy of 96.7% to 97.4% of the dose estimation with the proposed method and a down-sampling factor of 8 and 4, respectively. While maintaining a high accuracy, the proposed method provides for a tenfold speed-up. The overall findings suggest the conclusion that the proposed method has the potential to allow for further effciency.
CRIS-Team Scopus Importer:2020-04-21

Text-independent Speaker Identification using Temporal Patterns - Bocklet, Tobias; Maier, Andreas; Nöth, Elmar (Text, Speech and Dialogue, 2007)

The 19th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2016) - Ourselin, Sebastien; Sabuncu, Mert R.; Wells, William; Joskowicz, Leo; Unal, Gozde; Maier, Andreas (Medical Image Analysis, 2017)
CRIS-Team Scopus Importer:2022-06-05

THE 4-D+ NANOSCOPE: THE RESOLUTION FOR BIOLOGICAL SAMPLES - Kling, L.; Mill, L.; Grüneboom, Anika; Sarau, G.; Herrmann, Martin; Schett, Georg; Maier, Andreas; Christiansen, S. H. (ANNALS OF THE RHEUMATIC DISEASES, 2019)
CRIS-Team WoS Importer:2019-07-16

The Added Value of Volume-of-Interest C-Arm CT Imaging during Endovascular Treatment of Intracranial Aneurysms - Chintalapani, Gouthami; Maier, Andreas; Chinnadurai, Ponraj; Xia, Yan; Bauer, Sebastian; Shaltomi, Hashem; Morsi, Hesham; Mawad, Michel (American Journal of Neuroradiology, 2015)
UnivIS-Import:2016-02-10:Pub.2015.tech.IMMD.IMMD5.theadd

The Definition, Rationale, and Effects of Thresholding in OCT Angiography - Cole, Emily D.; Moult, Eric M.; Dang, Sabin; Choi, WooJhon; Ploner, Stefan; Lee, Byungkun; Louzada, Ricardo N.; Novais, Eduardo A.; Schottenhamml, Julia; Husvogt, Lennart; Maier, Andreas; Fujimoto, James G.; et al. (Ophthalmology Retina, 2017)
Purpose To examine the definition, rationale, and effects of thresholding in OCT angiography (OCTA). Design A theoretical description of OCTA thresholding in combination with qualitative and quantitative analysis of the effects of OCTA thresholding in eyes from a retrospective case series. Participants Four eyes were qualitatively examined: 1 from a 27-year-old control, 1 from a 78-year-old exudative age-related macular degeneration (AMD) patient, 1 from a 58-year-old myopic patient, and 1 from a 77-year-old nonexudative AMD patient with geographic atrophy (GA). One eye from a 75-year-old nonexudative AMD patient with GA was quantitatively analyzed. Main Outcome Measures A theoretical thresholding model and a qualitative and quantitative description of the dependency of OCTA on thresholding level. Results Due to the presence of system noise, OCTA thresholding is a necessary step in forming OCTA images; however, thresholding can complicate the relationship between blood flow and OCTA signal. Conclusions Thresholding in OCTA can cause significant artifacts, which should be considered when interpreting and quantifying OCTA images.
UnivIS-Import:2018-09-11:Pub.2017.tech.IMMD.IMMD5.thedef
Keywords: OCTA; OCT Angiography; thresholding

The Effect of Data Augmentation on Classification of Atrial Fibrillation in Short Single-Lead ECG Signals Using Deep Neural Networks - Nejati Hatamian, Faezeh; Ravikumar, Nishant; Vesal, Sulaiman; Kemeth, Felix P.; Struck, Matthias; Maier, Andreas (45th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2020), 2020)

The effect of speech pathology on automatic speaker verification: a large-scale study - Tayebi Arasteh, Soroosh; Weise, Tobias; Schuster, Maria; Nöth, Elmar; Maier, Andreas; Yang, Seung Hee (Scientific Reports, 2023)

The effects of RF coils and SAR supervision strategies for clinically applicable nonselective parallel-transmit pulses at 7 T - Herrler, Jürgen; Williams, Sydney N.; Liebig, Patrick; Ding, Belinda; McElhinney, Paul; Allwood-Spiers, Sarah; Meixner, Christian R.; Gunamony, Shajan; Nagel, Armin Michael; Dörfler, Arnd; Gumbrecht, Rene; Porter, David A.; et al. (Magnetic Resonance in Medicine, 2023)
Purpose: To investigate the effects of using different parallel-transmit (pTx) head coils and specific absorption rate (SAR) supervision strategies on pTx pulse design for ultrahigh-field MRI using a 3D-MPRAGE sequence. Methods: The PTx universal pulses (UPs) and fast online-customized (FOCUS) pulses were designed with pre-acquired data sets (B0, B1+ maps, specific absorption rate [SAR] supervision data) from two different 8 transmit/32 receive head coils on two 7T whole-body MR systems. For one coil, the SAR supervision model consisted of per-channel RF power limits. In the other coil, SAR estimations were done with both per-channel RF power limits as well as virtual observation points (VOPs) derived from electromagnetic field (EMF) simulations using three virtual human body models at three different positions. All pulses were made for nonselective excitation and inversion and evaluated on 132 B0, B1+, and SAR supervision datasets obtained with one coil and 12 from the other. At both sites, 3 subjects were examined using MPRAGE sequences that used UP/FOCUS pulses generated for both coils. Results: For some subjects, the UPs underperformed when simulated on a different coil from which they were derived, whereas FOCUS pulses still showed acceptable performance in that case. FOCUS inversion pulses outperformed adiabatic pulses when scaled to the same local SAR level. For the self-built coil, the use of VOPs showed reliable overestimation compared with the ground-truth EMF simulations, predicting about 52% lower local SAR for inversion pulses compared with per-channel power limits. Conclusion: FOCUS inversion pulses offer a low-SAR alternative to adiabatic pulses and benefit from using EMF-based VOPs for SAR estimation.
CRIS-Team Scopus Importer:2023-01-20
Keywords: fast online customized (FOCUS) pulses; parallel transmission (pTx); radiofrequency (RF) coils; UHF MRI; universal pulses (UPs); virtual observation points (VOPs)

The Hippocampus learns a language model with the help of the Successor Representation - Stöwer, Paul; Krauß, Patrick; Maier, Andreas (Montreal AI & Neuroscience 2021, 2021)

The Notary in the Haystack – Countering Class Imbalance in Document Processing with CNNs - Leipert, Martin; Vogeler, Georg; Seuret, Mathias; Maier, Andreas; Christlein, Vincent (Document Analysis Systems, 2020)
Notarial instruments are a category of documents. A notarial instrument can be distinguished from other documents by its notary sign, a prominent symbol in the certificate, which also allows to identify the document’s issuer. Naturally, notarial instruments are underrepresented in regard to other documents. This makes a classification difficult because class imbalance in training data worsens the performance of Convolutional Neural Networks. In this work, we evaluate different countermeasures for this problem. They are applied to a binary classification and a segmentation task on a collection of medieval documents. In classification, notarial instruments are distinguished from other documents, while the notary sign is separated from the certificate in the segmentation task. We evaluate different techniques, such as data augmentation, under- and oversampling, as well as regularizing with focal loss. The combination of random minority oversampling and data augmentation leads to the best performance. In segmentation, we evaluate three loss-functions and their combinations, where only class-weighted dice loss was able to segment the notary sign sufficiently. <br

Theoretically-exact filtered-backprojection reconstruction from real data on the line-ellipse-line trajectory - Guo, Zijia; Lauritsch, Guenter; Maier, Andreas; Noo, Frédéric (Proceedings Volume 11072, 2019)

The potential of omics technologies for the treatment of immune‐mediated inflammatory diseases - Anchang, Charles Gwellem; Xu, Cong; Raimondo, Maria Gabriella; Atreya, Raja; Maier, Andreas; Schett, Georg; Zaburdaev, Vasily; Rauber, Simon; Ramming, Andreas (International Journal of Molecular Sciences, 2021)
Immune‐mediated inflammatory diseases (IMIDs), such as inflammatory bowel diseases and inflammatory arthritis (e.g., rheumatoid arthritis, psoriatic arthritis), are marked by increasing worldwide incidence rates. Apart from irreversible damage of the affected tissue, the systemic nature of these diseases heightens the incidence of cardiovascular insults and colitis‐associated neo-plasia. Only 40–60% of patients respond to currently used standard‐of‐care immunotherapies. In addition to this limited long‐term effectiveness, all current therapies have to be given on a lifelong basis as they are unable to specifically reprogram the inflammatory process and thus achieve a true cure of the disease. On the other hand, the development of various OMICs technologies is consid-ered as “the great hope” for improving the treatment of IMIDs. This review sheds light on the pro-gressive development and the numerous approaches from basic science that gradually lead to the transfer from “bench to bedside” and the implementation into general patient care procedures.
CRIS-Team Scopus Importer:2021-07-30
Keywords: Advanced imaging technologies; Immune‐mediated inflammatory diseases (IMIDs); Single‐cell RNA sequencing; Spatial sequencing

The Random Forest Classifier in WEKA: Discussion and New Developments for Imbalanced Data - Amrehn, Mario; Mualla, Firas; Angelopoulou, Elli; Steidl, Stefan; Maier, Andreas (2018)
Data analysis and machine learning have become an integrative part of the modern scientific methodology, providing automated techniques to predict further information based on observations. One of these classification and regression techniques is the random forest approach. Those decision tree based predictors are best known for their good computational performance and scalability. However, in case of severely imbalanced training data, as often seen in medical studies' data with large control groups, the training algorithm or the sampling process has to be altered in order to improve the prediction quality for minority classes. In this work, a balanced random forest approach for WEKA is proposed. Furthermore, the prediction quality of the unmodified random forest implementation and the new balanced random forest version for WEKA are evaluated against reference implementations in R. Two-class problems on balanced data sets and imbalanced medical studies' data are investigated. A superior prediction quality using the proposed method for imbalanced data is shown compared to the other three techniques

The rapid rise of Fraktur - Weichselbaumer, Nikolaus; Seuret, Mathias; Limbach, Saskia; Hinrichsen, Lena; Maier, Andreas; Christlein, Vincent (7. Tagung des Verbands Digital Humanities im deutschsprachigen Raum, 2020)

The use of automatic speech recognition showing the influence of nasality on speech intelligibility - Mayr, Susanne; Burkhardt, Katja; Schuster, Maria; Rogler, Katrin; Maier, Andreas; Iro, Heinrich (European Archives of Oto-Rhino-Laryngology, 2010)

The Value of Volume of Interest (VOI) C-arm CT Imaging in the Endovascular Treatment of Intracranial Aneurysms - A Feasibility Study - Chintalapani, Gouthami; Chinnadurai, Ponraj; Maier, Andreas; Shaltomi, Hashem; Morsi, Hesham; Mawad, Michel (Proc. ASNR 50th Annual Meeting, 2012)

Three-dimensional anisotropic adaptive filtering of projection data for noise reduction in cone beam CT - Maier, Andreas; Wigström, Lars; Hofmann, Hannes; Hornegger, Joachim; Zhu, Lei; Strobel, Norbert; Fahrig, Rebecca (Medical Physics, 2011)
Purpose: The combination of quickly rotating C-arm gantry with digital flat panel has enabled the acquisition of three-dimensional data (3D) in the interventional suite. However, image quality is still somewhat limited since the hardware has not been optimized for CT imaging. Adaptive anisotropic filtering has the ability to improve image quality by reducing the noise level and therewith the radiation dose without introducing noticeable blurring. By applying the filtering prior to 3D reconstruction, noise-induced streak artifacts are reduced as compared to processing in the image domain. Methods: 3D anisotropic adaptive filtering was used to process an ensemble of 2D x-ray views acquired along a circular trajectory around an object. After arranging the input data into a 3D space (2D projections angle), the orientation of structures was estimated using a set of differently oriented filters. The resulting tensor representation of local orientation was utilized to control the anisotropic filtering. Low-pass filtering is applied only along structures to maintain high spatial frequency components perpendicular to these. The evaluation of the proposed algorithm includes numerical simulations, phantom experiments, and in-vivo data which were acquired using an AXIOM Artis dTA C-arm system (Siemens AG, Healthcare Sector, Forchheim, Germany). Spatial resolution and noise levels were compared with and without adaptive filtering. A human observer study was carried out to evaluate low-contrast detectability. Results: The adaptive anisotropic filtering algorithm was found to significantly improve low-contrast detectability by reducing the noise level by half (reduction of the standard deviation in certain areas from 74 to 30 HU). Virtually no degradation of high contrast spatial resolution was observed in the modulation transfer function (MTF) analysis. Although the algorithm is computationally intensive, hardware acceleration using Nvidia's CUDA Interface provided an 8.9-fold speed-up of the processing (from 1336 to 150 s). Conclusions: Adaptive anisotropic filtering has the potential to substantially improve image quality and/or reduce the radiation dose required for obtaining 3D image data using cone beam CT. © 2011 American Association of Physicists in Medicine.

Three-dimensional Monte Carlo-based voxel-wise tumor dosimetry in patients with neuroendocrine tumors who underwent 177Lu-DOTATOC therapy - Goetz, Th. I.; Lang, E. W.; Prante, Olaf; Maier, Andreas; Cordes, Michael; Kuwert, Torsten; Ritt, Philipp; Schmidkonz, Christian (Annals of Nuclear Medicine, 2020)
Background: Patients with advanced neuroendocrine tumors (NETs) of the midgut are suitable candidates for 177Lu-DOTATOC therapy. Integrated SPECT/CT systems have the potential to help improve the accuracy of patient-specific tumor dosimetry. Dose estimations to target organs are generally performed using the Medical Internal Radiation Dose scheme. We present a novel Monte Carlo-based voxel-wise dosimetry approach to determine organ- and tumor-specific total tumor doses (TTD). Methods: A cohort of 14 patients with histologically confirmed metastasized NETs of the midgut (11 men, 3 women, 62.3 ± 11.0 years of age) underwent a total of 39 cycles of 177Lu-DOTATOC therapy (mean 2.8 cycles, SD ± 1 cycle). After the first cycle of therapy, regions of interest were defined manually on the SPECT/CT images for the kidneys, the spleen, and all 198 tracer-positive tumor lesions in the field of view. Four SPECT images, taken at 4 h, 24 h, 48 h and 72 h after injection of the radiopharmaceutical, were used to determine their effective half-lives in the structures of interest. The absorbed doses were calculated by a three-dimensional dosimetry method based on Monte Carlo simulations. TTD was calculated as the sum of all products of single tumor doses with single tumor volumes divided by the sum of all tumor volumes. Results: The average dose values per cycle were 3.41 ± 1.28 Gy (1.91–6.22 Gy) for the kidneys, 4.40 ± 2.90 Gy (1.14–11.22 Gy) for the spleen, and 9.70 ± 8.96 Gy (1.47–39.49 Gy) for all 177Lu-DOTATOC-positive tumor lesions. Low- and intermediate-grade tumors (G 1–2) absorbed a higher TTD compared to high-grade tumors (G 3) (signed-rank test, p = < 0.05). The pre-therapeutic chromogranin A (CgA) value and the TTD correlated significantly (Pearson correlation: = 0.67, p = 0.01). Higher TTD resulted in a significant decrease of CgA after therapy. Conclusion: These results suggest that Monte Carlo-based voxel-wise dosimetry is a very promising tool for predicting the absorbed TTD based on histological and clinical parameters.
CRIS-Team Scopus Importer:2020-03-17
Keywords: Dosimetry; Monte Carlo simulations; Neuroendocrine tumors

Three-Step Volumetric Segmentation for Automated Shoe Fitting - Leipert, Martin; Herl, Gabriel; Stebani, Jannik; Zabler, Simon; Maier, Andreas (e-Journal of Nondestructive Testing 28, 2023)

Thrombus Detection in Non-Contrast Head CT using Graph Deep Learning - Popp, Antonia; Taubmann, Oliver; Thamm, Florian; Ditt, Hendrik; Maier, Andreas; Breininger, Katharina (Bildverarbeitung in der Medizin, 2022)
In case of an acute ischemic stroke, rapid diagnosis and removal of the occluding thrombus (blood clot) are crucial for a successful recovery. We present an automated thrombus detection system for noncontrast computed tomography (NCCT) images to improve the clinical workflow, where NCCT is typically acquired as a first-line imaging tool to identify the type of the stroke. The system consists of a candidate detection model and a subsequent classification model. The detection model generates a volumetric heatmap from the NCCT and extracts multiple potential clot candidates, sorted by their likeliness in descending order. The classification model performs reprioritization of these candidates using graph-based deep learning methods, where the candidates are no longer considered independently, but in a global context. It was optimized to classify the candidates as clot or no clot. The candidate detection model, which also serves as a baseline, yields a ROC AUC of 79.8%, which could be improved to 85.2% by the proposed graph-based classification model.<br

Tibia Cortical Bone Segmentation in Micro-CT and X-ray Microscopy Data Using a Single Neural Network - Aust, Oliver; Thies, Mareike; Weidner, Daniela; Wagner, Fabian; Pechmann, Sabrina; Mill, Leonid; Andreev, Darja; Miyagawa, Ippei; Krönke, Gerhard; Christiansen, Silke; Uderhardt, Stefan; Maier, Andreas; et al. (Informatik aktuell, 2022)
CRIS-Team Scopus Importer:2022-05-06

Torsional Heart Motion in Cone-beam Computed Tomography Reconstruction - Unberath, Mathias; Mentl, Katrin; Taubmann, Oliver; Achenbach, Stephan; Fahrig, Rebecca; Hornegger, Joachim; Maier, Andreas (Proceedings of the Fully3D, 2015)
UnivIS-Import:2017-12-18:Pub.2015.tech.IMMD.IMMD5.torsio_3

Toward analyzing mutual interference on infrared-enabled depth cameras - Adams Seewald, Lucas; Facco Rodrigues, Vinicius; Ollenschläger, Malte; Stoffel Antunes, Rodolfo; Andre da Costa, Cristiano; da Rosa Righi, Rodrigo; da Silveira Junior, Luiz Gonzaga; Maier, Andreas; Eskofier, Björn; Fahrig, Rebecca (Computer Vision and Image Understanding, 2019)
Camera setups with multiple devices are a key aspect of ambient monitoring applications. These types of setups can result in data corruption when applied to recent RGB-D camera models because of mutual interference of the infrared light emitters employed by such devices. Consequently, the behavior of such interference must be appropriately evaluated to provide data that will allow monitoring systems to handle possible errors introduced into the data captured by depth sensors. However, multi-device setups have been explored in few studies in the current literature, especially in terms of the detailed measurements of the interference’s effect during long-term usage of RGB-D cameras. In this context, a methodology to evaluate the effect of mutual interference on the accuracy and precision of measured depth values is proposedin this article. The results of a series of experiments with different setups based on multiple depth cameras are explored. These setups include three devices that were widely used in studies in the computer vision literature related to depth imaging: the Microsoft Kinect v2 and two Intel RealSense models: R200 and D415. The experimental results indicate that the Kinect v2 yields considerably more stable depth readings than the RealSense R200 in single-camera scenarios, even considering the influence of the warm-up time that is characteristic of time-of-flight devices such as the Kinect v2. In multi-device setups, the Kinect v2 displays periodic peaks of mutual interference that increase in intensity depending on the distance between the cameras, with short-range setups yielding higher interference peaks. Further, the addition of more devices can potentially increase the duration of some interference peaks, albeit their intensity is not greatly affected. In long-range setups, the measured interference is small considering the experiments’ length, with the proportion of bad pixels among all captured frames ranging from 3.74% to 3.97% in a setup comprising three depth cameras. In turn, multi-device setups comprising the RealSense models are not affected by prejudicial interference peaks. In long-range setups, the instability of the R200 leads to its results being less accurate and precise than those of the Kinect v2 under mutual interference. However, in close range multi-device setups, the high interference peaks observed with the Kinect v2 render the RealSense models a more stable alternative.
Keywords: Computer vision; Interference; Multi-camera; Depth camera; Evaluation

Toward Bridging the Simulated-to-Real Gap: Benchmarking Super-Resolution on Real Data - Köhler, Thomas; Bätz, Michel; Naderi Boldaji, Farzad; Kaup, André; Maier, Andreas; Rieß, Christian (IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019)
UnivIS-Import:2019-08-15:Pub.2019.tech.IMMD.IMMD5.toward_0

Toward Quantitative Optical Coherence Tomography Angiography: Visualizing Blood Flow Speeds in Ocular Pathology Using Variable Interscan Time Analysis - Ploner, Stefan; Moult, Eric M.; Choi, WooJhon; Waheed, Nadia K.; Lee, Byungkun; Novais, Eduardo A.; Cole, Emily D.; Potsaid, Benjamin; Husvogt, Lennart; Schottenhamml, Julia; Maier, Andreas; Rosenfeld, Philip; et al. (Retina (Philadelphia, Pa.), 2016)
Purpose: Currently available optical coherence tomography angiography systems provide information about blood flux but only limited information about blood flow speed. The authors develop a method for mapping the previously proposed variable interscan time analysis (VISTA) algorithm into a color display that encodes relative blood flow speed. Methods: Optical coherence tomography angiography was performed with a 1,050 nm, 400 kHz A-scan rate, swept source optical coherence tomography system using a 5 repeated B-scan protocol. Variable interscan time analysis was used to compute the optical coherence tomography angiography signal from B-scan pairs having 1.5 millisecond and 3.0 milliseconds interscan times. The resulting VISTA data were then mapped to a color space for display. Results: The authors evaluated the VISTA visualization algorithm in normal eyes (n = 2), nonproliferative diabetic retinopathy eyes (n = 6), proliferative diabetic retinopathy eyes (n = 3), geographic atrophy eyes (n = 4), and exudative age-related macular degeneration eyes (n = 2). All eyes showed blood flow speed variations, and all eyes with pathology showed abnormal blood flow speeds compared with controls. Conclusion: The authors developed a novel method for mapping VISTA into a color display, allowing visualization of relative blood flow speeds. The method was found useful, in a small case series, for visualizing blood flow speeds in a variety of ocular diseases and serves as a step toward quantitative optical coherence tomography angiograph
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.toward_9
Keywords: Age-related macular degeneration; Choroidal neovascularization; Nascent geographic atrophy; Ocular blood flow; Optical coherence tomography (OCT); Optical coherence tomography angiography (OCTA); Variable interscan time analysis (VISTA)

Towards accelerated quantitative sodium MRI at 7 T in the skeletal muscle: Comparison of anisotropic acquisition- and compressed sensing techniques - Utzschneider, Matthias; Müller, Max; Gast, Lena; Lachner, Sebastian; Behl, Nicolas G.R.; Maier, Andreas; Uder, Michael; Nagel, Armin Michael (Magnetic Resonance Imaging, 2021)
Purpose: To compare three anisotropic acquisition schemes and three compressed sensing (CS) approaches for accelerated tissue sodium concentration (TSC) quantification using 23Na MRI at 7 T. Materials and methods: Three anisotropic 3D-radial acquisition sequences were evaluated using simulations, phantom- and in vivo TSC measurements: An anisotropic density-adapted 3D-radial sequence (3DPR-C), a 3D acquisition-weighted density-adapted stack-of-stars sampling scheme (SOS) and a SOS approach with golden-ratio rotation (SOS-GR). Eight healthy volunteers were examined at a 7 Tesla MRI system. TSC measurements of the calf were conducted with a nominal spatial resolution of Δx = (3.0 × 3.0 × 15.0) mm3 and a field of view of (156.0 × 156.0 × 240.0) mm3 for multiple undersampling factors (USF). Three CS reconstructions were evaluated: Total variation CS (TV-CS), 3D dictionary-learning compressed sensing (3D-DLCS) and TV-CS with a block matching prior (TV-BL-CS). Results of the simulations and measurements were compared to a simulated ground truth (GT) or a fully sampled reference measurement (FS), respectively. The deviation of the mean TSC evaluated in multiple ROI (mEGT/FS) and the normalized root-mean-squared error (NRMSE) for simulations were evaluated for CS and NUFFT reconstructions. Results: In simulations, the SOS-GR yielded the lowest NRMSE and mEGT (< 4%) with NUFFT for an acquisition time (TA) of less than 2 min. CS further improved the results. In simulations and measurements, the best TSC quantification results were obtained with 3D-DLCS and SOS-GR (lowest NRMSE, mEGT < 2.6% in simulations, mEGT < 10.7% for phantom measurements and mEFS < 6% in vivo) with an USF = 4.1 (TA < 2 min). TV-CS showed no or only slight improvements to NUFFT. The results of TV-BL-CS were similar to 3D-DLCS. Discussion: The TA for TSC measurements could be reduced to less than 2 min by using adapted sequences such as SOS-GR and CS reconstruction approaches such as 3D-DLCS or TV-BL-CS, while the quantitative accuracy stays comparable to a fully sampled NUFFT reconstruction (approx. 8 min TA). In future, the lower TA could improve clinical applicability of TSC measurements.
CRIS-Team Scopus Importer:2020-11-06
Keywords: 7 tesla; Anisotropic acquisition schemes; Compressed sensing; Image reconstruction; Skeletal muscle; Sodium (

Towards a dual phase grating interferometer on clinical hardware - Bopp, Johannes; Ludwig, Veronika; Gallersdörfer, Michael; Seifert, Maria; Pelzer, Georg; Maier, Andreas; Anton, Gisela; Riess, Christian (Medical Imaging 2018: Physics of Medical Imaging, 2018)
Keywords: Phase contrast imaging; Talbot-Lau; Dual phase grating; Grating based interferometry

Towards a language-independent intelligibility assessment of children with cleft lip and palate - Bocklet, Tobias; Maier, Andreas; Riedhammer, Korbinian Thomas; Nöth, Elmar (Proceedings of WOCCI 2009, 2009)
We describe a novel evaluation system for the intelligibility assessment of children with CLP on standardized tests. The system is solely based on standard cepstral features in form of MFCCs. No other information like word alignments is used. So the system can be easily adapted to other languages. For each child one GMM is created by adaptation of a UBM to the speaker-specific MFCCs. The components of this GMM are concatenated in order to create a so-called GMM supervector. These GMM supervectors are then used as meta features for an SVR. We evaluated our language-independent system on two different datasets of children suffering from CLP. One dataset contains recordings of 35 German children, where the children named different pictograms. The other dataset contains recordings of 14 Italian speaking children, who repeated standardized sentences. On both datasets we achieved high correlations: up to 0.81 for the German dataset and 0.83 for the Italian dataset. Copyright 2009 ACM.
Keywords: Acoustic analysis; Cleft lip and palate; Gaussian mixture models; Support vector regression

Towards an analytic model: Describing the effect of scan angle and slice thickness on the in-plane spatial resolution of calcications in digital breast tomosynthesis - Luckner, Christoph; Schebesch, Frank; Mertelmeier, Thomas; Fieselmann, Andreas; Maier, Andreas; Ritschl, Ludwig (Proc. of SPIE, 2018)
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.toward_6
Keywords: DBT; tomosynthesis; scanning angle; slice thickness; in-plane resolution; analytic model; calcication

Towards Arbitrary Noise Augmentation-Deep Learning for Sampling from Arbitrary Probability Distributions - Horger, Felix; Würfl, Tobias; Christlein, Vincent; Maier, Andreas (MACHINE LEARNING FOR MEDICAL IMAGE RECONSTRUCTION, MLMIR 2018, 2018)
CRIS-Team WoS Importer:2019-08-09

Towards Automatic Abdominal Multi-Organ Segmentation in Dual Energy CT using Cascaded 3D Fully Convolutional Network - Chen, Shuqing; Roth, Holger; Dorn, Sabrina; May, Matthias; Lell, Michael; Kachelriess, Marc; Oda, Hirohisa; Mori, Kensaku; Maier, Andreas; Cavallaro, Alexander Josef (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.toward_16

Towards cartilage diagnosis in X-ray phase-contrast interferometry - Bopp, Johannes; Bartl, Peter; Ritschl, Ludwig; Radicke, Marcus; Maier, Andreas; Anton, Gisela; Riess, Christian (14th IEEE International Symposium on Biomedical Imaging, 2017)

Towards Clinical Application of a Laplace Operator-based Region of Interest Reconstruction Algorithm in C-arm CT - Xia, Yan; Hofmann, Hannes; Dennerlein, Frank; Müller, Kerstin; Schwemmer, Chris; Bauer, Sebastian; Chintalapani, Gouthami; Chinnadurai, Ponraj; Hornegger, Joachim; Maier, Andreas (IEEE Transactions on Medical Imaging, 2014)
It is known that a reduction of the field-of-view in 3-D X-ray imaging is proportional to a reduction in radiation dose. The resulting truncation, however, is incompatible with conventional reconstruction algorithms. Recently, a novel method for region of interest reconstruction that uses neither prior knowledge nor 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 2-D Laplace filtering and a 2-D Radon-based residual filtering step. In this paper, we present two variants of the original ATRACT. One is based on expressing the residual filter as an efficient 2-D convolution with an analytically derived kernel. The second variant is to apply ATRACT in 1-D to further reduce computational complexity. The proposed algorithms were evaluated by using a reconstruction benchmark, as well as two clinical data sets. The results are encouraging since the proposed algorithms achieve a speed-up factor of up to 245 compared to the 2-D Radon-based ATRACT. Reconstructions of high accuracy are obtained, e.g., even real-data reconstruction in the presence of severe truncation achieve a relative root mean square error of as little as 0.92% with respect to nontruncated data. © 1982-2012 IEEE.
UnivIS-Import:2015-03-09:Pub.2014.tech.IMMD.IMMD5.toward_3
Keywords: C-arm CT; dose reduction; image reconstruction; region of interest; truncation; truncation artifact

Towards context-sensitive CT imaging organ-specific image formation for single (SECT) and dual energy computed tomography (DECT) - Dorn, Sabrina; Chen, Shuqing; Sawall, Stefan; Maier, Joscha; Knaup, Michael; Uhrig, Monika; Schlemmer, Heinz-Peter; Maier, Andreas; Lell, Michael; Kachelriess, Marc (Medical Physics, 2018)
PurposeThe purpose of this study was to establish a novel paradigm to facilitate radiologists' workflow combining mutually exclusive CT image properties that emerge from different reconstructions, display settings and organ-dependent spectral evaluation methods into a single context-sensitive imaging by exploiting prior anatomical information.MethodsThe CT dataset is segmented and classified into different organs, for example, the liver, left and right kidney, spleen, aorta, and left and right lung as well as into the tissue types bone, fat, soft tissue, and vessels using a cascaded three-dimensional fully convolutional neural network (CNN) consisting of two successive 3D U-nets. The binary organ and tissue masks are transformed to tissue-related weighting coefficients that are used to allow individual organ-specific parameter settings in each anatomical region. Exploiting the prior knowledge, we develop a novel paradigm of a context-sensitive (CS) CT imaging consisting of a prior-based spatial resolution (CSR), display (CSD), and dual energy evaluation (CSDE). The CSR locally emphasizes desired image properties. On a per-voxel basis, the reconstruction most suitable for the organ, tissue type, and clinical indication is chosen automatically. Furthermore, an organ-specific windowing and display method is introduced that aims at providing superior image visualization. The CSDE analysis allows to simultaneously evaluate multiple organs and to show organ-specific DE overlays wherever appropriate. The ROIs that are required for a patient-specific calibration of the algorithms are automatically placed into the corresponding anatomical structures. The DE applications are selected and only applied to the specific organs based on the prior knowledge. The approach is evaluated using patient data acquired with a dual source CT system. The final CS images simultaneously link the indication-specific advantages of different parameter settings and result in images combining tissue-related desired image properties.ResultsA comparison with conventionally reconstructed images reveals an improved spatial resolution in highly attenuating objects and in air while the compound image maintains a low noise level in soft tissue. Furthermore, the tissue-related weighting coefficients allow for the combination of varying settings into one novel image display. We are, in principle, able to automate and standardize the spectral analysis of the DE data using prior anatomical information. Each tissue type is evaluated with its corresponding DE application simultaneously.ConclusionThis work provides a proof of concept of CS imaging. Since radiologists are not aware of the presented method and the tool is not yet implemented in everyday clinical practice, a comprehensive clinical evaluation in a large cohort might be topic of future research. Nonetheless, the presented method has potential to facilitate workflow in clinical routine and could potentially improve diagnostic accuracy by improving sensitivity for incidental findings. It is a potential step toward the presentation of evermore increasingly complex information in CT and toward improving the radiologists workflow significantly since dealing with multiple CT reconstructions may no longer be necessary. The method can be readily generalized to multienergy data and also to other modalities.
Keywords: CT; CNN segmentation; dual energy; image display; image formation

Towards End-to-End Deep Learning-based Writer Identification - Wang, Zhenghua; Maier, Andreas; Christlein, Vincent (INFORMATIK 2020, 2021)

Towards Fast Biomechanical Modeling of Soft Tissue Using Neural Networks - Meister, Felix; Passerini, Tiziano; Mihalef, Viorel; Tuysuzoglu, Ahmet; Maier, Andreas; Mansi, Tommaso (Medical Imaging meets NeurIPS Workshop 2018, 2018)
To date, the simulation of organ deformations for applications like therapy planning or image-guided interventions is calculated by solving the elastodynamics equations. While efficient solvers have been proposed for fast simulations, methods that are both real-time and accurate are still an open challenge. An ideal, interactive solver would be able to provide physically and numerically accurate results at high frame rate, which requires efficient force computation and time integration. Towards this goal, we explore in this paper for the first time the use of neural networks to directly learn the underlying biomechanics. Given a 3D mesh of a soft tissue segmented from medical images, we train a neural network to predict vertex-wise accelerations for a large time step based on the current state of the system. The model is trained using the deformation of a bar under torsion, and evaluated on different motions, geometries, and hyperelastic material models. For predictions of ten times the original time step we observed a mean error of 0.017mm ± 0.014 (0.032) at a mesh size of 50mm x 50mm x 100mm. Predictions at 20dt yield an error of 2.10mm ± 1.73 (4.37) and by further increasing the prediction time step the maximum error rises to 38.3mm due to an artificial stiffening. In all experiments our proposed method stayed stable, while the reference solver fails to converge. Our experiments suggest that it is possible to directly learn the mechanical simulation and open further investigations for the direct application of machine learning to speed-up biophysics solvers.<br

Towards Image Caption Generation for Art Historical Data - Madhu, Prathmesh; Gupta, Jahnvi; Kosti, Ronak Vijaypal; Bell, Peter; Maier, Andreas; Christlein, Vincent (2020)

Towards Intelligent Robust Detection of Anatomical Structures in Incomplete Volumetric Data - Ghesu, Florin-Cristian; Georgescu, Bogdan; Grbic, Sasa; Maier, Andreas; Hornegger, Joachim; Comaniciu, Dorin (Medical Image Analysis, 2018)
Robust and fast detection of anatomical structures represents an important component of medical image analysis technologies. Current solutions for anatomy detection are based on machine learning, and are generally driven by suboptimal and exhaustive search strategies. In particular, these techniques do not effectively address cases of incomplete data, i.e., scans acquired with a partial field-of-view. We address these challenges by following a new paradigm, which reformulates the detection task to teaching an intelligent artificial agent how to actively search for an anatomical structure. Using the principles of deep reinforcement learning with multi-scale image analysis, artificial agents are taught optimal navigation paths in the scale-space representation of an image, while accounting for structures that are missing from the field-of-view. The spatial coherence of the observed anatomical landmarks is ensured using elements from statistical shape modeling and robust estimation theory. Experiments show that our solution outperforms marginal space deep learning, a powerful deep learning method, at detecting different anatomical structures without any failure. The dataset contains 5043 3D-CT volumes from over 2000 patients, totaling over 2,500,000 image slices. In particular, our solution achieves 0% false-positive and 0% false-negative rates at detecting whether the landmarks are captured in the field-of-view of the scan (excluding all border cases), with an average detection accuracy of 2.78 mm. In terms of runtime, we reduce the detection-time of the marginal space deep learning method by 20–30 times to under 40 ms, an unmatched performance for high resolution incomplete 3D-CT data.
UnivIS-Import:2019-08-15:Pub.2018.tech.IMMD.IMMD5.toward_1
Keywords: Deep learning; Deep reinforcement learning; Incomplete 3D-data; M-estimator sample consensus; Multi-scale detection; Real-time detection; Robust statistical shape-modeling; Scale-space modeling

Towards In-Vivo X-Ray Nanoscopy: Acquisition Parameters vs. Image Quality - Mill, Leonid; Kling, Lasse; Grüneboom, Anika; Schett, Georg; Christiansen, Silke; Maier, Andreas (Informatik aktuell, 2019)
X-ray microscopy is a powerful imaging technique that permits the investigation of specimen on nanoscale with resolution of up to 700 nm in 3-D. In the context of bio-medical research this is a promising technology that allows to study the microstructure of biological tissues. However, X-ray microscopy (XRM) systems are not designed for in-vivo applications and are mainly used in the field of material sciences in which dose is irrelevant. High resolution scans may take up to 10 hours. Our long-term goal is to utilize this modality to study the effects of disease dynamics and treatment in-vivo on mice bones. Therefore, a first step towards this ambitious goal is to evaluate the current state-of-the-art to determine the required system parameters. In this work, we investigate the impact of different XRM settings on the image quality. By changing various acquisition parameters such as exposure time, voltage, current and number of projections, we simulate the outcome of XRM scans, while reducing the X-ray energy. We base our simulations on a high resolution ex-vivo scan of a mouse tibia. The resulting reconstructions are evaluated qualitatively as well as quantitatively by calculating the contrast-to-noise ratio (CNR). We demonstrate that we can reach comparable image quality while reducing the total X-ray energy which forms a foundation towards the upcoming experiments.
CRIS-Team Scopus Importer:2019-05-14

Towards in-vivo X-ray nanoscopy: The effect of motion on image quality - Mill, Leonid; Bier, Bastian; Syben-Leisner, Christopher; Kling, Lasse; Grüneboom, Anika; Christiansen, Silke; Schett, Georg; Maier, Andreas (Informatik aktuell, 2018)
Novel X-Ray Microscopy (XRM) systems allow to study the internal structure of a specimen on nanoscale. A possible use of this non-destructive technology is motivated in the medical research area. In-Vivo investigation of medication over a period of time and its effects on perfusion and bony structure might lead to a better understanding of drug mechanisms and diseases like Osteoporosis and could lead to new approaches to their treatment. The first step towards in-vivo XRM imaging is to investigate the suitability of recent XRM systems for this task and subsequently to determine the system parameters. In this context, the impact of mice motion on the image quality is studied in this work. This paper aims to simulate the effects of breathing motion and muscle relaxation of the mice on the reconstructed images, which already effects the projection images. We therefore assume a mouse’s respiration motion pattern, which happens four time during a single projection acquisitions, and the muscle relaxation movement due to anesthesia and simulate its impacts on image quality. Additionally, we show that a frame rate of at least 16 fps is needed to capture in-vivo movements in order to apply state-of-the-art motion correction methods.<br

Towards Material Decomposition on Large Field-of-View Flat Panel Photon-Counting Detectors - First in-vivo Results - Müller, Kerstin; Moiz, Ahmad; Spahn, Martin; Choi, Jang-Hwan; Reitz, Silke; Köster, Niko; Lu, Yanye; Fahrig, Rebecca; Maier, Andreas (Proceedings of the fourth international conference on image formation in x-ray computed tomography, 2016)
UnivIS-Import:2017-12-18:Pub.2016.tech.IMMD.IMMD5.toward_8

Towards Monitoring of Children's Speech - A Case Study - Maier, Andreas; Schuster, Maria; Nöth, Elmar (Workshop on Child, Computer, and Interaction 2008, 2008)

Towards more Reality in the Recognition of Emotional Speech - Schuller, Björn; Seppi, Dino; Batliner, Anton; Maier, Andreas; Steidl, Stefan (ICASSP, 2007 IEEE International Conference on Acoustics, Speech, and Signal Processing, Proceedings, 2007)

Towards Mouse Bone X-ray Microscopy Scan Simulation - Fu, Weilin; Mill, Leonid; Seitz, Stephan; Geimer, Tobias; Kling, Lasse; Possart, Dennis; Christiansen, Silke; Maier, Andreas (Informatik aktuell, 2021)
CRIS-Team Scopus Importer:2021-04-19

Towards Optimal Channels for a Detection Channelized Hotelling Observer - Schebesch, Frank; Maier, Andreas (3rd Conference on Image-Guided Interventions & Fokus Neuroradiologie, 2017)
UnivIS-Import:2018-02-22:Pub.2017.tech.IMMD.IMMD5.toward_13

Towards quantification of kidney stones using X-ray dark-field tomography - Hu, Shiyang; Yang, F.; Kaufmann, R.; Griffa, M.; Anton, Gisela; Maier, Andreas; Riess, Christian (14th IEEE International Symposium on Biomedical Imaging, 2017)

TOWARDS ROBUST AUTOMATIC EVALUATION OF PATHOLOGIC TELEPHONE SPEECH - Riedhammer, Korbinian Thomas; Stemmer, Georg; Haderlein, Tino; Schuster, Maria; Rosanowski, Frank; Nöth, Elmar; Maier, Andreas (Proceedings of ASRU 2007, 2007)
For many aspects of speech therapy an objective evaluation of the intelligibility of a patient's speech is needed. We investigate the evaluation of the intelligibility of speech by means of automatic speech recognition. Previous studies have shown that measures like word accuracy are consistent with human experts' ratings. To ease the patient's burden, it is highly desirable to conduct the assessment via phone. However, the telephone channel influences the quality of the speech signal which negatively affects the results. To reduce inaccuracies, we propose a combination of two speech recognizers. Experiments on two sets of pathological speech show that the combination results in consistent improvements in the correlation between the automatic evaluation and the ratings by human experts. Furthermore, the approach leads to reductions of 10% and 25% of the maximum error of the intelligibility measure. © 2007 IEEE.
UnivIS-Import:2015-10-26:Pub.2007.tech.IMMD.IMMD5.toward_7
Keywords: Speech Intelligibility; Pathologic Speech; Speech Recognition

Towards Robust Distant-Talking Automatic Speech Recognition in Reverberant Environments - Riedhammer, Korbinian Thomas; Stemmer, Georg; Haderlein, Tino; Schuster, Maria; Rosanowski, Frank; Nöth, Elmar; Maier, Andreas (Proceedings of ASRU 2007, 2007)
UnivIS-Import:2015-04-02:Pub.2008.tech.IE.LEN.toward

Towards (smart) Full-body X-ray Images - Luckner, Christoph; Mertelmeier, Thomas; Maier, Andreas; Ritschl, Ludwig (Bildverarbeitung für die Medizin 2018. Informatik aktuell, 2018)
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.toward_9

Towards Super-resolution CEST MRI for Visualization of Small Structures - Folle, Lukas; Tkotz, Katharina; Gadjimuradov, Fasil; Kapsner, Lorenz; Fabian, Moritz; Bickelhaupt, Sebastian; Simon, David; Kleyer, Arnd; Krönke, Gerhard; Zaiß, Moritz; Nagel, Armin Michael; Maier, Andreas (Bildverarbeitung für die Medizin 2022, 2022)
The onset of rheumatic diseases such as rheumatoid arthritis is typically subclinical, which results in challenging early detection of the disease. However, characteristic changes in the anatomy can be detected using imaging techniques such as MRI or CT. Modern imaging techniques such as chemical exchange saturation transfer (CEST) MRI drive the hope to improve early detection even further through the imaging of metabolites in the body. To image small structures in the joints of patients, typically one of the first regions where changes due to the disease occur, a high resolution for the CEST MR imaging is necessary. Currently, however, CEST MR suffers from an inherently low resolution due to the underlying physical constraints of the acquisition. In this work we compared established up-sampling techniques to neural network-based super-resolution approaches. We could show, that neural networks are able to learn the mapping from low-resolution to high-resolution unsaturated CEST images considerably better than present methods. On the test set a PSNR of 32.29 dB (+10%), a NRMSE of 0.14 (+28%), and a SSIM of 0.85 (+15%) could be achieved using a ResNet neural network, improving the baseline considerably. This work paves the way for the prospective investigation of neural networks for super-resolution CEST MRI and, followingly, might lead to a earlier detection of the onset of rheumatic diseases.
Keywords: Deep Learning; Rheumatic diseases; Reconstruction

Towards the Automatic Classification of Reading Disorders in Continuous Text Passages - Maier, Andreas; Bocklet, Tobias; Hönig, Florian Thomas; Horndasch, Stefanie; Nöth, Elmar (Text, Speech and Dialogue, 2009)

Towards the Automatic Evaluation of Dysarthric Speech - Maier, Andreas; Nöth, Elmar; Rosanowski, Frank; Sous-Kulke, Christa; Schupp, Wilfried (Technically Assisted Rehabilitation - TAR 2009, 2009)

Towards Understanding Preservation of Periodic Object Motion in Computed Tomography - Schirrmacher, Franziska; Taubmann, Oliver; Unberath, Mathias; Maier, Andreas (Bildverarbeitung für die Medizin 2017, 2017)
UnivIS-Import:2018-09-11:Pub.2017.tech.IMMD.IMMD5.toward_1

Traditional machine learning for limited angle tomography - Huang, Yixing; Lu, Yanye; Taubmann, Oliver; Lauritsch, Günter; Maier, Andreas (International Journal of Computer Assisted Radiology and Surgery, 2018)
Purpose The application of traditional machine learning techniques, in the form of regression models based on conventional, “hand-crafted” features, to artifact reduction in limited angle tomography is investigated. Methods Mean-variation-median (MVM), Laplacian, Hessian, and shift-variant data loss (SVDL) features are extracted from the images reconstructed from limited angle data. The regression models linear regression (LR), multilayer perceptron (MLP), and reduced-error pruning tree (REPTree) are applied to predict artifact images. Results REPTree learns artifacts best and reaches the smallest root-mean-square error (RMSE) of 29 HU for the Shepp–Logan phantom in a parallel-beam study. Further experiments demonstrate that the MVM and Hessian features complement each other, whereas the Laplacian feature is redundant in the presence of MVM. In fan-beam, the SVDL features are also beneficial. A preliminary experiment on clinical data in a fan-beam study demonstrates that REPTree can reduce some artifacts for clinical data. However, it is not sufficient as a lot of incorrect pixel intensities still remain in the estimated reconstruction images. Conclusion REPTree has the best performance on learning artifacts in limited angle tomography compared with LR and MLP. The features of MVM, Hessian, and SVDL are beneficial for artifact prediction in limited angle tomography. Preliminary experiments on clinical data suggest that the investigation on more features is necessary for clinical applications of REPTre
UnivIS-Import:2018-09-05:Pub.2018.tech.IMMD.IMMD5.tradit_4
Keywords: Machine learning; limited angle tomography; decision tree

Traditional Machine Learning Techniques for Streak Artifact Reduction in Limited Angle Tomography - Huang, Yixing; Lu, Yanye; Taubmann, Oliver; Lauritsch, Günter; Maier, Andreas (Bildverarbeitung für die Medizin 2018 Algorithmen - Systeme - Anwendungen, 2018)
In this work, the application of traditional machine learning techniques, in the form of regression models based on conventional, “hand-crafted” features, to streak reduction in limited angle tomography is investigated. Specifically, linear regression (LR), multi-layer perceptron (MLP), and reduced-error pruning tree (REPTree) are investigated. When choosing the mean-variation-median (MVM), Laplacian, and Hessian features, REPTree learns streak artifacts best and reaches the smallest root-mean-square error (RMSE) of 29HU for the Shepp-Logan phantom. Further experiments demonstrate that the MVM and Hessian features complement each other, whereas the Laplacian feature is redundant in the presence of MVM. Preliminary experiments on clinical data suggests that further investigation of clinical applications using REPTree may be worthwhile.<br
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.tradit
Keywords: machine learning; limited angle tomography; artifact reduction

Trainable joint bilateral filters for enhanced prediction stability in low-dose CT - Wagner, Fabian; Thies, Mareike; Denzinger, Felix; Gu, Mingxuan; Patwari, Mayank; Ploner, Stefan; Maul, Noah; Pfaff, Laura; Huang, Yixing; Maier, Andreas (Scientific Reports, 2022)
Low-dose computed tomography (CT) denoising algorithms aim to enable reduced patient dose in routine CT acquisitions while maintaining high image quality. Recently, deep learning (DL)-based methods were introduced, outperforming conventional denoising algorithms on this task due to their high model capacity. However, for the transition of DL-based denoising to clinical practice, these data-driven approaches must generalize robustly beyond the seen training data. We, therefore, propose a hybrid denoising approach consisting of a set of trainable joint bilateral filters (JBFs) combined with a convolutional DL-based denoising network to predict the guidance image. Our proposed denoising pipeline combines the high model capacity enabled by DL-based feature extraction with the reliability of the conventional JBF. The pipeline’s ability to generalize is demonstrated by training on abdomen CT scans without metal implants and testing on abdomen scans with metal implants as well as on head CT data. When embedding RED-CNN/QAE, two well-established DL-based denoisers in our pipeline, the denoising performance is improved by 10%/82% (RMSE) and 3%/81% (PSNR) in regions containing metal and by 6%/78% (RMSE) and 2%/4% (PSNR) on head CT data, compared to the respective vanilla model. Concluding, the proposed trainable JBFs limit the error bound of deep neural networks to facilitate the applicability of DL-based denoisers in low-dose CT pipelines.<br

Training a tunable, spatially-adaptive denoiser without clean targets - Pfaff, Laura; Hoßbach, Julian; Preuhs, Elisabeth; Arroyo Camejo, Silvia; Nickel, Marcel Dominik; Maier, Andreas; Wuerfl, Tobias (ISMRM, 2022)

Training Deep Learning Models for 2D Spine X-rays Using Synthetic Images and Annotations Created from 3D CT Volumes - Sukesh, Richin; Fieselmann, Andreas; Jaganathan, Srikrishna; Shetty, Karthik; Kaergel, Rainer; Kordon, Florian Johannes; Kappler, Steffen; Maier, Andreas (Bildverarbeitung für die Medizin 2022, 2022)
When training deep learning models in the medical domain, one is always burdened with the task of obtaining reliable medical data annotated by experts. However, the availability of annotated data is often limited. To overcome such limitations, this paper addresses the idea of using synthetic spine X-ray data to train a deep learning model to aid in the detection of vertebrae. For this purpose, a pipeline for automatic generation of synthetic datasets comprising synthetic Xray images and their corresponding annotations is developed and evaluated. The results of these experiments show improvements in detection rates of the model when synthetic X-ray data is added to the training dataset.<br
Keywords: X-ray Imaging; Spine; Object Detection; Synthetic Data

Transferability of Deep Learning Algorithms for Malignancy Detection in Confocal Laser Endomicroscopy Images from Different Anatomical Locations of the Upper Gastrointestinal Tract - Aubreville, Marc; Goncalves, Miguel; Knipfer, Christian; Oetter, Nicolai; Würfl, Tobias; Neumann, Helmut; Stelzle, Florian; Bohr, Christopher; Maier, Andreas (Communications in Computer and Information Science, 2019)
Squamous Cell Carcinoma (SCC) is the most common cancer type of the epithelium and is often detected at a late stage. Besides invasive diagnosis of SCC by means of biopsy and histo-pathologic assessment, Confocal Laser Endomicroscopy (CLE) has emerged as noninvasive method that was successfully used to diagnose SCC in vivo. For interpretation of CLE images, however, extensive training is required, which limits its applicability and use in clinical practice of the method. To aid diagnosis of SCC in a broader scope, automatic detection methods have been proposed. This work compares two methods with regard to their applicability in a transfer learning sense, i.e. training on one tissue type (from one clinical team) and applying the learnt classification system to another entity (different anatomy, different clinical team). Besides a previously proposed, patch-based method based on convolutional neural networks, a novel classification method on image level (based on a pre-trained Inception V.3 network with dedicated preprocessing and interpretation of class activation maps) is proposed and evaluated. The newly presented approach improves recognition performance, yielding accuracies of 91.63% on the first data set (oral cavity) and 92.63% on a joint data set. The generalization from oral cavity to the second data set (vocal folds) lead to similar area-under-the-ROC curve values than a direct training on the vocal folds data set, indicating good generalization.
CRIS-Team Scopus Importer:2019-09-17
Keywords: Confocal Laser Endomicroscopy; Head and neck squamous cell carcinoma; Transfer learning

Transfer Learning for Olfactory Object Detection - Zinnen, Mathias; Madhu, Prathmesh; Bell, Peter; Maier, Andreas; Christlein, Vincent (Digital Humanities 2022 Conference Abstracts, 2022)
We investigate the effect of style and category similarity in multiple datasets used for object detection pretraining. We find that including an additional stage of object-detection pretraining can increase the detection performance considerably. While our experiments suggest that style similarities between pre-training and target datasets are less important than matching categories, further experiments are needed to verify this hypothesis.
Keywords: transfer learning; object detection; computational humanities; computational heritage

Transferring Quantified Emotion Knowledge for the Detection of Depression in Alzheimer’s Disease Using Forestnets - Pérez Toro, Paula Andrea; Rodriguez Salas, Dalia; Arias Vergara, Tomás; Bayerl, Sebastian P.; Klumpp, Philipp; Riedhammer, Korbinian Thomas; Schuster, Maria; Nöth, Elmar; Maier, Andreas; Orozco Arroyave, Juan Rafael (ICASSP 2023, 2023)
Progressive loss of memory is the most known symptom of Alzheimer’s Disease (AD); however, it also affects other cognitive skills and leads to depression symptoms. This paper presents a transfer learning strategy for automatically detecting AD and depression in AD patients using acoustic information and ForestNet, an artificial neural network that allows computing the contribution of a set of features to a model’s decision. The methodology consists of training ForestNet with a dataset commonly used for emotion recognition; then, we fine-tune the pre-trained model to detect AD and depression in AD. We trained the models with several acoustic features commonly used for emotion and AD applications. Unweighted average recalls of up to 0.87 were achieved to classify the disease and up to 0.82 to detect depression in AD. Our results indicate that the information obtained from the Arousal Valence plane may be suitable for discriminating and analyzing depression in AD.<br
Keywords: Emotion recognition; Analytical models; Computational modeling; Transfer learning; Forestry; Artificial neural networks; Depression

Transient Hemodynamics Prediction Using an Efficient Octree-Based Deep Learning Model - Maul, Noah; Zinn, Katharina; Wagner, Fabian; Thies, Mareike; Rohleder, Maximilian; Pfaff, Laura; Kowarschik, Markus; Birkhold, Annette; Maier, Andreas (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2023)
Patient-specific hemodynamics assessment could support diagnosis and treatment of neurovascular diseases. Currently, conventional medical imaging modalities are not able to accurately acquire high-resolution hemodynamic information that would be required to assess complex neurovascular pathologies. Instead, computational fluid dynamics (CFD) simulations can be applied to tomographic reconstructions to obtain clinically relevant information. However, three-dimensional (3D) CFD simulations require enormous computational resources and simulation-related expert knowledge that are usually not available in clinical environments. Recently, deep-learning-based methods have been proposed as CFD surrogates to improve computational efficiency. Nevertheless, the prediction of high-resolution transient CFD simulations for complex vascular geometries poses a challenge to conventional deep learning models. In this work, we present an architecture that is tailored to predict high-resolution (spatial and temporal) velocity fields for complex synthetic vascular geometries. For this, an octree-based spatial discretization is combined with an implicit neural function representation to efficiently handle the prediction of the 3D velocity field for each time step. The presented method is evaluated for the task of cerebral hemodynamics prediction before and during the injection of contrast agent in the internal carotid artery (ICA). Compared to CFD simulations, the velocity field can be estimated with a mean absolute error of 0.024 m s - 1, whereas the run time reduces from several hours on a high-performance cluster to a few seconds on a consumer graphical processing unit.
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Keywords: Hemodynamics; Octree; Operator learning

Truncation correction for VOI C-arm CT using scattered radiation - Bier, Bastian; Maier, Andreas; Hofmann, Hannes; Schwemmer, Chris; Xia, Yan; Struffert, Tobias; Hornegger, Joachim (Medical Imaging 2013: Physics of Medical Imaging, 2013)
In C-arm computed tomography, patient dose reduction by volume-of-interest (VOI) imaging is of increasing interest for many clinical applications. A remaining limitation of VOI imaging is the truncation artifact when reconstructing a 3D volume. It can either be cupping towards the boundaries of the field-of-view (FOV) or an incorrect offset in the Hounsfield values of the reconstructed voxels. In this paper, we present a new method for correction of truncation artifacts in a collimated scan. When axial or lateral collimation are applied, scattered radiation still reaches the detector and is recorded outside of the FOV. If the full area of the detector is read out we can use this scattered signal to estimate the truncated part of the object. We apply three processing steps: detection of the collimator edge, adjustment of the area outside the FOV, and interpolation of the collimator edge. Compared to heuristic truncation correction methods we were able to reconstruct high contrast structures like bones outside of the FOV. Inside the FOV we achieved similar reconstruction results as with water cylinder truncation correction. These preliminary results indicate that scattered radiation outside the FOV can be used to improve image quality and further research in this direction seems beneficial. © 2013 SPIE.

Truncation Correction for X-ray Phase-Contrast Region-of-Interest Tomography - Felsner, Lina; Käppler, Sebastian; Maier, Andreas; Riess, Christian (IEEE Transactions on Computational Imaging, 2020)

Truncation Correction using a 3D Filter for Cone-beam CT - Xia, Yan; Maier, Andreas; Dennerlein, Frank; Hornegger, Joachim (Fully3D 2013, 2013)

Truncation Robust C-Arm CT Reconstruction for Dynamic Collimation Acquisition Schemes - Kästner, Thomas; Hornegger, Joachim; Maier, Andreas; Xia, Yan; Bauer, Sebastian (Proceedings in Bildverarbeitung für die Medizin 2015, 2015)
UnivIS-Import:2015-04-17:Pub.2015.tech.IMMD.IMMD5.trunca_3

Tutorial: Deep Learning - Advancing the State-of-the-Art in Medical Image Analysis - Christlein, Vincent; Ghesu, Florin-Cristian; Würfl, Tobias; Maier, Andreas; Isensee, Fabian; Neher, Peter; Maier-Hein, Klaus (Bildverarbeitung für die Medizin 2017, 2017)
UnivIS-Import:2018-09-06:Pub.2017.tech.IMMD.IMMD5.tutori

TV or not TV? That is the Question - Riess, Christian; Berger, Martin; Wu, Haibo; Manhart, Michael; Fahrig, Rebecca; Maier, Andreas (Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2013)

Two-Dimensional Respiratory-Motion Characterization for Continuous MR Measurements Using Pilot Tone Navigation - Schroeder, Lea; Wetzl, Jens; Maier, Andreas; Rehner, Robert; Fenchel, Matthias; Speier, Peter (Proceedings of the 24th Annual Meeting of the ISMRM (ISMRM 2016), 2016)
UnivIS-Import:2017-01-09:Pub.2016.tech.IMMD.IMMD5.twodim

UI-Net: Interactive Artificial Neural Networks for Iterative Image Segmentation Based on a User Model - Amrehn, Mario; Gaube, Sven; Unberath, Mathias; Schebesch, Frank; Horz, Tim; Strumia, Maddalena; Steidl, Stefan; Kowarschik, Markus; Maier, Andreas (EG VCBM 2017, 2017)
For complex segmentation tasks, fully automatic systems are inherently limited in their achievable accuracy for extracting relevant objects. Especially in cases where only few data sets need to be processed for a highly accurate result, semi-automatic segmentation techniques exhibit a clear benefit for the user. One area of application is medical image processing during an intervention for a single patient. We propose a learning-based cooperative segmentation approach which includes the computing entity as well as the user into the task. Our system builds upon a state-of-the-art fully convolutional artificial neural network (FCN) as well as a simple rule based active user model for training. During the segmentation process, a user of the trained system can iteratively add additional hints in form of pictorial scribbles as seed points into the FCN system to achieve an interactive and precise segmentation result. The segmentation quality of interactive FCNs is evaluated. Iterative FCN approaches can yield superior results compared to networks without the user input channel component, due to a consistent improvement in segmentation quality after each interaction.
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.uineti
Keywords: CNN; FCN; Machine Learning; Interactive; User; Semi-automatic; Feedback; Seeds; Segmentation; Medical Imaging

Ultrahigh-Resolution Optical Coherence Tomography Investigation of Age-Related Changes in the Photoreceptors, Retinal Pigment Epithelium and Bruch's Membrane - Chen, Siyu; Abu-Qamar, Omar; Moult, Eric; Ploner, Stefan; Lee, Byungkun; Maier, Andreas; Waheed, Nadia; Fujimoto, James (INVESTIGATIVE OPHTHALMOLOGY & VISUAL SCIENCE, 2020)
CRIS-Team WoS Importer:2020-09-25

Ultra low‐parameter denoising: Trainable bilateral filter layers in computed tomography - Wagner, Fabian; Thies, Mareike; Gu, Mingxuan; Huang, Yixing; Pechmann, Sabrina; Patwari, Mayank; Ploner, Stefan; Aust, Oliver; Uderhardt, Stefan; Schett, Georg; Christiansen, Silke H.; Maier, Andreas (Medical Physics, 2022)
Background Computed tomography (CT) is widely used as an imaging tool to visualize three-dimensional structures with expressive bone-soft tissue contrast. However, CT resolution can be severely degraded through low-dose acquisitions, highlighting the importance of effective denoising algorithms. Purpose Most data-driven denoising techniques are based on deep neural networks and, therefore, contain hundreds of thousands of trainable parameters, making them incomprehensible and prone to prediction failures. Developing understandable and robust denoising algorithms achieving state-of-the-art performance helps to minimize radiation dose while maintaining data integrity. Methods This work presents an open-source CT denoising framework based on the idea of bilateral filtering. We propose a bilateral filter that can be incorporated into any deep learning pipeline and optimized in a purely data-driven way by calculating the gradient flow toward its hyperparameters and its input. Denoising in pure image-to-image pipelines and across different domains such as raw detector data and reconstructed volume, using a differentiable backprojection layer, is demonstrated. In contrast to other models, our bilateral filter layer consists of only four trainable parameters and constrains the applied operation to follow the traditional bilateral filter algorithm by design. Results Although only using three spatial parameters and one intensity range parameter per filter layer, the proposed denoising pipelines can compete with deep state-of-the-art denoising architectures with several hundred thousand parameters. Competitive denoising performance is achieved on x-ray microscope bone data and the 2016 Low Dose CT Grand Challenge data set. We report structural similarity index measures (SSIM) of 0.7094 and 0.9674 and peak signal-to-noise ratio (PSNR) values of 33.17 and 43.07 on the respective data sets. Conclusions Due to the extremely low number of trainable parameters with well-defined effect, prediction reliance and data integrity is guaranteed at any time in the proposed pipelines, in contrast to most other deep learning-based denoising architecture
Keywords: known operator learning; low-dose CT; bilateral filter; denoising

Ultrasound Breast Lesion Detection using Extracted Attention Maps from a Weakly Supervised Convolutional Neural Network - Rodriguez Salas, Dalia; Seuret, Mathias; Vesal, Sulaiman; Maier, Andreas (Bildverarbeitung für die Medizin 2021, 2021)
In order to detect lesions on medical images, deep learning models commonly require information about the size of the lesion, either through a bounding box or through the pixel-/voxel-wise annotation of the lesion, which is in turn extremely expensive to produce in most cases. In this paper, we aim at demonstrating that by having a single central point per lesion as ground truth for 3D ultrasounds, accurate deep learning models for lesion detection can be trained, leading to precise visualizations using Grad-CAM. From a set of breast ultrasound volumes, healthy and diseased patches were used to train a deep convolutional neural network. On the one hand, each diseased patch contained in its central area a lesion's center annotated by experts. On the other hand, healthy patches were extracted from random regions of ultrasounds taken from healthy patients. An AUC of 0.92 and an accuracy of 0.87 was achieved on test patches.<br
Keywords: attention maps; week supervision; lesion detection

Unbiased high-dimensional flow cytometry identified NK and DC immune cell signature in Luminal A-type and triple negative breast cancer - Heger, Lukas; Heidkamp, Gordon Frederik; Amon, Lukas; Nimmerjahn, Falk; Bäuerle, Tobias; Maier, Andreas; Erber, Ramona; Hartmann, Arndt; Hack, Carolin; Rübner, Matthias; Hübner, Hanna; Fasching, Peter; et al. (OncoImmunology, 2024)
Breast cancer is the most common malignancy in women worldwide and a highly heterogeneous disease. Four different subtypes are described that differ in the expression of hormone receptors as well as the growth factor receptor HER2. Treatment modalities and survival rate depend on the subtype of breast cancer. However, it is still not clear which patients benefit from immunotherapeutic approaches such as checkpoint blockade. Thus, we aimed to decipher the immune cell signature of the different breast cancer subtypes based on high-dimensional flow cytometry followed by unbiased approaches. Here, we show that the frequency of NK cells is reduced in Luminal A and B as well as triple negative breast cancer and that the phenotype of residual NK cells is changed toward regulatory CD11b−CD16− NK cells. Further, we found higher frequencies of PD-1+ CD4+ and CD8+ T cells in triple negative breast cancer. Moreover, while Luminal A-type breast cancer was enriched for CD14+ cDC2 (named type 3 DC (DC3)), CD14− cDC2 (named DC2) were more frequent in triple negative breast cancer. In contrast, HER2-enriched breast cancer did not show major alterations in the composition of the immune cell compartment in the tumor microenvironment. These findings suggest that patients with Luminal A- and B-type as well as triple negative breast cancer might benefit from immunotherapeutic approaches targeting NK cells.
CRIS-Team Scopus Importer:2024-01-12
Keywords: Breast cancer; immune cell signature; NK cells; PD-1; tumor microenvironment

Understanding Compositional Structures in Art Historical Images Using Pose and Gaze Priors - Madhu, Prathmesh; Marquart, Tilman; Kosti, Ronak Vijaypal; Bell, Peter; Maier, Andreas; Christlein, Vincent (Computer Vision – ECCV 2020 Workshops. ECCV 2020, 2020)
Image compositions as a tool for analysis of artworks is of extreme significance for art historians. These compositions are useful in analyzing the interactions in an image to study artists and their artworks. Max Imdahl in his work called Ikonik, along with other prominent art historians of the 20th" role="presentation" style="display: inline-table; line-height: normal; letter-spacing: normal; word-spacing: normal; overflow-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border-width: 0px; border-style: initial; position: relative;">thth century, underlined the aesthetic and semantic importance of the structural composition of an image. Understanding underlying compositional structures within images is challenging and a time consuming task. Generating these structures automatically using computer vision techniques (1) can help art historians towards their sophisticated analysis by saving lot of time; providing an overview and access to huge image repositories and (2) also provide an important step towards an understanding of man made imagery by machines. In this work, we attempt to automate this process using the existing state of the art machine learning techniques, without involving any form of training. Our approach, inspired by Max Imdahl’s pioneering work, focuses on two central themes of image composition: (a) detection of action regions and action lines of the artwork; and (b) pose-based segmentation of foreground and background. Currently, our approach works for artworks comprising of protagonists (persons) in an image. In order to validate our approach qualitatively and quantitatively, we conduct a user study involving experts and non-experts. The outcome of the study highly correlates with our approach and also demonstrates its domain-agnostic capability. We have open-sourced the code: https://github.com/image-compostion-canvas-group/image-compostion-canvas<br
Keywords: Compositional structures; Art history; Computer vision

U-Net for Multi-Organ Segmentation of SPECT Projection Data - Mürschberger, Nina; Reymann, Maximilian; Cachovan, Michal; Ritt, Philipp; Vija, Hans; Maier, Andreas (U-Net for Multi-Organ Segmentation of SPECT Projection Data, 2020)
This work explores the usage of deep learning techniques for segmentation of anatomical structures on SPECT data. Previous works applied deep learning techniques on CT scans of anatomical information and afterwards registered the resulting segmentation mask on the SPECT data. We train a U-Net on 2D projection data extracted from Lu-177 MELP SPECT scans aim to predict the areas corresponding to liver, spleen and kidney. The challenging acquisition technique comes along with low resolution, low contrast between the abdominal organs and variations in size, shapes and location due to the differences in patients` anatomies. We use the dice similarity coefficient to evaluate the network. Visual validation, confusion matrix and recall shall evaluate the worthiness of further investigations in deep learning techniques used for nuclear medicine. We achieve a mean dice similarity coefficient of 72% and a validation loss of 11%, encouraging us to further investigate our approach.<br
Keywords: SPECT; Segmentation; Deep Learning; Lu-177

U-Net for SPECT Image Denoising - Reymann, Maximilian; Würfl, Tobias; Stimpel, Bernhard; Ritt, Philipp; Cachovan, Michal; Vija, A. Hans; Maier, Andreas (2019 IEEE Nuclear Science Symposium and Medical Imaging Conference Proceedings (NSS/MIC), 2019)
Single Photon Emitted Computed Tomography (SPECT) is characterized by low photon counts and high degrees of image noise. In this work we investigate deep image postprocessing methods for improving image quality in SPECT projections and propose a new architecture. Specifically, the residual U-Net proposed by Heinrich et al. for low-dose Computed Tomography (CT) and the Convolutional Denoising Autoencoder (CNN DAE) by Gondara et al. were tested for their performance for denoising SPECT projections. Reference images without noise and scatter were obtained from SPECT Monte Carlo simulations of 24 different XCAT phantoms of brains, lungs, livers and skeletons using the SIMIND software. These clean images were then used as training target for the neural networks. Additionally, we propose a U-Net architecture that is more suited for SPECT image denoising, consisting of 4 layers, each with 2 blocks of convolution, batch normalization and ReLU. Our results showed that the proposed U-Net outperforms the residual U-Net and the CNN DAE with a PSNR of 38.25 dB, compared to 27.95 dB and 28.25 dB, respectively. It was shown that the characteristics of the SPECT data posed a challenge to the neural networks, as there are only few distinct photon count values at the detector available and these are superimposed with a high degree of image noise, compared to low-dose CT. Our proposed network was able to decrease the noise within the phantom and additionally decreased the background noise in the image. The image quality was improved by 14.15 dB compared to the noisy input image, demonstrating the suitability of our network. We believe that with increased size and diversity of our dataset we can further boost the performance.<br
Keywords: SPECT; Denoising; Monte Carlo Simulation; XCAT Phantom; SIMIND; U-Net

Universal Plug'n'PEAKS - Towards easy Deployment of Multi-modal Tele-medicine - Janu, Thomas; Spiegl, Werner; Soutschek, Stefan; Maier, Andreas; Steidl, Stefan; Nöth, Elmar (Proceedings of the 5th Russian-Bavarian Conference on Biomedical Engineering, 2009)

Unsupervised Deep Learning for Advanced Forming Limit Analysis in Sheet Metal: A Tensile Test-Based Approach - Thamm, Aleksandra; Thamm, Florian; Sawodny, Annette; Zeitler, Sally; Merklein, Marion; Maier, Andreas (Materials, 2023)
An accurate description of the formability and failure behavior of sheet metal materials is essential for an optimal forming process design. In this respect, the forming limit curve (FLC) based on the Nakajima test, which is determined in accordance with DIN EN ISO 12004-2, is a wide-spread procedure for evaluating the formability of sheet metal materials. Thereby the FLC is affected by influences originating from intrinsic factors of the Nakajima test-setup, such as friction, which leads to deviations from the linear strain path, biaxial prestress and bending superposition. These disadvantages can be circumvented by an alternative test combination of uniaxial tensile test and hydraulic bulge test. In addition, the forming limit capacity of many lightweight materials is underestimated using the cross-section method according to DIN EN ISO 12004-2, due to the material-dependent occurrence of multiple strain maxima during forming or sudden cracking without prior necking. In this regard, machine learning approaches have a high potential for a more accurate determination of the forming limit curve due to the inclusion of other parameters influencing formability. This work presents a machine learning approach focused on uniaxial tensile tests to define the forming limit of lightweight materials and high-strength steels. The transferability of an existing weakly supervised convolutional neural network (CNN) approach was examined, originally designed for Nakajima tests, to uniaxial tensile tests. Additionally, a stereo camera-based method for this purpose was developed. In our evaluation, we train and test materials, including AA6016, DX54D, and DP800, through iterative data composition, using cross-validation. In the context of our stereo camera-based approach, strains for different materials and thicknesses were predicted. In this cases, our method successfully predicted the major strains with close agreement to ISO standards. For DX54D, with a thickness of 0.8 mm, the prediction was 0.659 (compared to ISO’s 0.664). Similarly, for DX54D, 2.0 mm thickness, the predicted major strain was 0.780 (compared to ISO 0.705), and for AA6016, at 1.0 mm thickness, a major strain of 0.314 (in line with ISO 0.309) was estimated. However, for DP800 with a thickness of 1.0 mm, the prediction yielded a major strain of 0.478 (as compared to ISO 0.289), indicating a divergence from the ISO standard in this particular case. These results in general, generated with the CNN stereo camera-based approach, underline the quantitative alignment of the approach with the cross-section metho
Keywords: forming limit curve; pattern recognition; sheet metal forming; machine learning

Unsupervised denoising of prostate DWI - Pfaff, Laura; Wagner, Fabian; Hoßbach, Julian; Preuhs, Elisabeth; Gadjimuradov, Fasil; Benkert, Thomas; Nickel, Marcel Dominik; Wuerfl, Tobias; Maier, Andreas (ISMRM, 2023)

Unsupervised detection of Small Hyperreflective Features in Ultrahigh Resolution Optical Coherence Tomography - Reimann, Marcel; Won, Jungeun; Takahashi, Hiroyuki; Yaghy, Antonio; Hwang, Yunchan; Ploner, Stefan; Lin, Junhong; Girgis, Jessica; Lam, Kenneth; Chen, Siyu; Waheed, Nadia K.; Maier, Andreas; et al. (Informatik aktuell, 2023)
Recent advances in optical coherence tomography such as the development of high speed ultrahigh resolution scanners and corresponding signal processing techniques may reveal new potential biomarkers in retinal diseases. Newly visible features are, for example, small hyperreflective specks in age-related macular degeneration. Identifying these new markers is crucial to investigate potential association with disease progression and treatment outcomes. Therefore, it is necessary to reliably detect these features in 3D volumetric scans. Because manual labeling of entire volumes is infeasible a need for automatic detection arises. Labeled datasets are often not publicly available and there are usually large variations in scan protocols and scanner types. Thus, this work focuses on an unsupervised approach that is based on local peak-detection and random walker segmentation to detect small features on each B-scan of the volume.
Created from Fastlane, Scopus look-up

Unsupervised Feature Learning for Writer Identification and Writer Retrieval - Christlein, Vincent; Gropp, Martin; Fiel, Stefan; Maier, Andreas (2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), 2017)
UnivIS-Import:2018-02-22:Pub.2017.tech.IMMD.IMMD5.unsupe_6

Unsupervised Learning for Robust Respiratory Signal Estimation from X-Ray Fluoroscopy - Fischer, Peter; Pohl, Thomas; Faranesh, Anthony; Maier, Andreas; Hornegger, Joachim (IEEE Transactions on Medical Imaging, 2017)
UnivIS-Import:2017-07-10:Pub.2017.tech.IMMD.IMMD5.unsupe
Keywords: Clustering; Dimensionality reduction; Fluoroscopy; Patch-based; Respiratory signal; X-ray

Unsupervised OCT Denoising using speckle split - Schottenhamml, Julia; Würfl, Tobias; Ploner, Stefan; Husvogt, Lennart; Hohberger, Bettina; Fujimoto, James G.; Maier, Andreas (INVESTIGATIVE OPHTHALMOLOGY & VISUAL SCIENCE, 2022)
CRIS-Team WoS Importer:2022-09-30

Unsupervised Super Resolution in X-ray Microscopy using a Cycle-consistent Generative Model - Raghunath, Adarsh; Wagner, Fabian; Thies, Mareike; Gu, Mingxuan; Pechmann, Sabrina; Aust, Oliver; Weidner, Daniela; Neag, Georgiana; Schett, Georg; Christiansen, Silke H.; Uderhardt, Stefan; Maier, Andreas (Bildverarbeitung für die Medizin 2023, 2023)
X-ray microscopy (XRM) is a tomographic imaging modality that has gained interest in the context of understanding bone-related diseases on the micro scale due to its high spatial resolution and strong bone to soft tissue contrast. Although in-vivo imaging of bone structures on the micro scale is desired from a medical perspective, high radiation dose so-far prohibits imaging living animals. Research has been focused on generating high-quality reconstructions while maintaining a low X-ray dosage. However, low dose acquisitions result in noisy images with a lower resolution. This study focuses on using an unsupervised deep-learning approach to accurately reconstruct high-resolution (HR) XRM images from their noisy low-resolution (LR) counterparts. We consider an unsupervised approach in a general case where paired data (low-/high resolution pairs) are unavailable. We propose the use of a cycle-consistent generative adversarial network (GAN) for this super resolution task which is to learn the mapping from noisy LR to HR images. Quantitative and qualitative assessments show that our method produces accurate high-resolution XRM reconstructions from their noisy low-resolution counterparts, increasing the peak signal-to-noise ratio (PSNR)/structural similarity index (SSIM) from 18.15/0.52 (baseline) to 31.94/0.73 (proposed method). We believe that our proposed XRM super resolution pipeline provides a valuable tool toward high-resolution in-vivo XRM imaging.<br

Unsupervised Unstained Cell Detection by SIFT Keypoint Clustering and Self-labeling Algorithm - Mualla, Firas; Schöll, Simon; Sommerfeldt, Björn; Maier, Andreas; Steidl, Stefan; Buchholz, Rainer; Hornegger, Joachim (Lecture Notes in Computer Science, Volume 8675, MICCAI 2014 Proceedings, Part III, 2014)
We propose a novel unstained cell detection algorithm based on unsupervised learning. The algorithm utilizes the scale invariant feature transform (SIFT), a self-labeling algorithm, and two clustering steps in order to achieve high performance in terms of time and detection accuracy. Unstained cell imaging is dominated by phase contrast and bright field microscopy. Therefore, the algorithm was assessed on images acquired using these two modalities. Five cell lines having in total 37 images and 7250 cells were considered for the evaluation: CHO, L929, Sf21, HeLa, and Bovine cells. The obtained F-measures were between 85.1 and 89.5. Compared to the state-of-the-art, the algorithm achieves very close F-measure to the supervised approaches in much less time.
UnivIS-Import:2015-04-16:Pub.2014.tech.IMMD.IMMD5.unsupe_6

Unsupervised unstained cell detection by SIFT keypoint clustering and self-labeling algorithm. - Muallal, Firas; Schöll, Simon; Sommerfeldt, Björn; Maier, Andreas; Steidl, Stefan; Buchholz, Rainer; Hornegger, Joachim (International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2014)
We propose a novel unstained cell detection algorithm based on unsupervised learning. The algorithm utilizes the scale invariant feature transform (SIFT), a self-labeling algorithm, and two clustering steps in order to achieve high performance in terms of time and detection accuracy. Unstained cell imaging is dominated by phase contrast and bright field microscopy. Therefore, the algorithm was assessed on images acquired using these two modalities. Five cell lines having in total 37 images and 7250 cells were considered for the evaluation: CHO, L929, Sf21, HeLa, and Bovine cells. The obtained F-measures were between 85.1 and 89.5. Compared to the state-of-the-art, the algorithm achieves very close F-measure to the supervised approaches in much less time.
CRIS-Team Scopus Importer:2022-08-05

Updating Siamese trackers using peculiar mixup - Wu, Fei; Zhang, Jianlin; Xu, Zhiyong; Maier, Andreas; Christlein, Vincent (Applied Intelligence, 2023)
Siamese network-based trackers are commonly used for tracking due to their balanced accuracy and speed. However, the fixed template from the initial frame is used to localize the target in the current frame, and never being updated in Siamese trackers. This loses the power of updating the appearance model online and easily induces tracking failures for the intrinsic properties of tracking processes such as constantly changing scenes and endless distractors. To address this problem, we propose a simple yet effective visual tracking framework for updating the appearance model in a novel formulation that introduces a peculiar mixup method both for training and inference phase of Siamese trackers (named PMUM-Siam). It consists of a template matching network and a mixup module. First, instead of center-cropping the inexact predictions for tracking, we use the template matching network which trained with predefined anchor boxes to learn to select the best candidate from similar distractors. Second, the mixup module is used to fuse and update the trade-off appearance model between the best candidate and the groundtruth. Our method greatly enhances the capability of target identification and target localization in Siamese trackers. To further demonstrate the generality of the proposed method, we integrate our PMUM-Siam into two representative Siamese trackers (SiamFC and SiamRPN+ +). Extensive experimental results and comparisons on five challenging object tracking benchmarks including OTB-2013, OTB-2015, OTB-50, VOT-2016, and VOT-2018 show that PMUM-Siam achieves leading performance with an average speed of 300 FPS.
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Keywords: Object tracking; Siamese network; Template update

User Loss A Forced-Choice-Inspired Approach to Train Neural Networks Directly by User Interaction - Zarei, Shahab; Stimpel, Bernhard; Syben-Leisner, Christopher; Maier, Andreas (Informatik aktuell, 2019)
In this paper, we investigate whether is it possible to train a neural network directly from user inputs. We consider this approach to be highly relevant for applications in which the point of optimality is not well-defined and user-dependent. Our application is medical image denoising which is essential in fluoroscopy imaging. In this field every user, i.e. physician, has a different flavor and image quality needs to be tailored towards each individual. To address this important problem, we propose to construct a loss function derived from a forced-choice experiment. In order to make the learning problem feasible, we operate in the domain of precision learning, i.e., we inspire the network architecture by traditional signal processing methods in order to reduce the number of trainable parameters. The algorithm that was used for this is a Laplacian pyramid with only six trainable parameters. In the experimental results, we demonstrate that two image experts who prefer different filter characteristics between sharpness and de-noising can be created using our approach. Also models trained for a specific user perform best on this users test data. This approach opens the way towards implementation of direct user feedback in deep learning and is applicable for a wide range of application.
CRIS-Team Scopus Importer:2019-05-14

Using Associative Classification and Odds Ratios for In-Hospital Mortality Risk Estimation - Haas, Oliver; Maier, Andreas; Rothgang, Eva (Workshop on Interpretable ML in Healthcare at International Conference on Machine Learning (ICML)., 2021)
We propose a novel method based on associative classification in combination with odds ratios, a well-understood epidemiological metric, as an interpretable method for in-hospital mortality estimation, which is influenced by thousands of clinical variables. We tested and validated the method for cases in intensive and emergency care. The resulting model achieves an area under the receiver operating characteristic curve of 0.98. The model is easy to interpret in the form of one-to-one rules and the corresponding odds ratios. This study shows that associative classification combined with epidemiological metrics can be used as effective and interpretable machine learning models in the presence of outcomes that are influenced by thousands of variables.

Using Forestnets for Partial Fine-Tuning Prior to Breast Cancer Detection in Ultrasounds - Rodriguez Salas, Dalia; Öttl, Mathias; Seuret, Mathias; Packhäuser, Kai; Maier, Andreas (2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), 2023)
Ultrasound imaging is the most used technique for first investigating suspicions of breast lesions. In recent years, many deep learning techniques have been developed to analyze ultrasound imagery automatically. In this paper, we present results obtained on breast lesion detection using FasterRCNN with a ResNet18 as the backbone. We pre-trained the backbone on classifying benign and malignant lesions using an extremely sparse classification head called ForestNet instead of the usual fully-connected head. Results on classification showed that the inherent sparsity of ForestNet led to reduced overfitting and kept untouched roughly 7% of the millions of parameters in the ResNet18 backbone. Our experiments were run on two different datasets. On classification, our modified ResNet-18 architecture classified correctly 85.9% and 91.3%, while the usual architecture achieved 75.6% and 80.6%, respectively. Regarding detection, the average precision on the two datasets rises from 80.4% and 79.2% to respectively 87.3% and 87.0% using our approach.<br
CRIS-Team Scopus Importer:2023-10-06
Keywords: Breast Ultrasound; ForestNets; Lesion Detection; Partial Fine-tuning; Sparsity

Using Medical Image Reconstruction Methods for Denoising of OCTA Data - Husvogt, Lennart; Ploner, Stefan; Moult, Eric M.; Alibhai, A. Yasin; Schottenhamml, Julia; Duker, Jay S.; Waheed, Nadia K.; Fujimoto, James G.; Maier, Andreas (Investigative Ophthalmology & Visual Science, 2019)
Purpose : A commonly used method to generate OCT angiography (OCTA) data is to compute the amplitude decorrelation of repeated B-scans. Despite its prevalence, to our knowledge, amplitude decorrelation, and related metrics were developed heuristically, and lack complete theoretical descriptions. Outside of OCTA, a variety of compressed sensing-based image reconstruction algorithms have been successfully applied to magnetic resonance imaging and computed tomography. Inspired by the work in these fields, we developed a probabilistic model for amplitude decorrelation. This, and models for speckle variance and interframe variance, enable an objective-function minimization approach to OCTA data generation with optimized noise characteristics. Methods : We generated ground-truth images by registering and merging 10 consecutively acquired 3x3mm OCTA volumes from a healthy volunteer. A compressed-sensing-based denoising method with a 3D median filter for regularization was used for reconstruction. Results : Figure 1 shows the decreasing mean squared error, compared to our ground-truth data, of our reconstruction algorithm over 100 iterations, indicating increasingly improved noise characteristics. Figure 2 shows corresponding representative en face retinal OCTA images from the reconstruction; ground truth data are shown in panel A, and test data in panel B. Compared to median filtering (panel C), our OCTA reconstruction decreases noise while minimizing image blurring. Reconstruction results in panels D through F show how the reconstruction can be used to optimize the denoising between the original volume and the median-filtered volume. Conclusions : State-of-the-art reconstruction techniques, such as compressed sensing, can be adopted from other medical imaging fields to improve the quality of OCTA dat