Motion Gradients for Epipolar Consistency - Preuhs, Alexander; Manhart, Michael; Hoppe, Elisabeth; Kowarschik, Markus; Maier, Andreas (15th International Meeting on Fully Three-Dimensional Image Reconstruction, 2019)

Move Over There: One-Click Deformation Correction for Image Fusion During Endovascular Aortic Repair - Breininger, Katharina; Pfister, Marcus; Kowarschik, Markus; Maier, Andreas (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2020)
Fusing intraoperative X-ray with information from preoperative computed tomography for endovascular aortic repair has been shown to reduce radiation exposure, need for contrast agent, and procedure time. However, due to the instruments inserted during the intervention, the vasculature deforms and the fusion loses accuracy. In this paper, we propose an approach to use minimal user input obtained from a single 2D image for deformation correction in 3D. We integrate the 2D positional information as a projective constraint in an as-rigid-as-possible deformation model that allows for intraoperative correction of the deformation. Our method achieves clinically relevant accuracies while keeping user inputs to a minimum. We are able to recover the deformation at the right and left internal iliac bifurcation up to a 3D error of 1.9mm, with an error of 0.5mm orthogonal to the viewing direction, and an error of 1.7mm in depth, while keeping the mean computation time below 6s.
CRIS-Team Scopus Importer:2020-11-20
Keywords: Deformation modeling; Endovascular aortic repair; EVAR; Fluoroscopy; Image fusion

Move Over There One-click Deformation Correction for Image Fusion during Endovascular Aortic Repair - Breininger, Katharina; Pfister, Marcus; Kowarschik, Markus; Maier, Andreas (Informatik aktuell, 2021)
CRIS-Team Scopus Importer:2021-04-19

MR-contrast-aware image-to-image translations with generative adversarial networks - Denck, Jonas; Guehring, Jens; Maier, Andreas; Rothgang, Eva (International Journal of Computer Assisted Radiology and Surgery, 2021)
Purpose: A magnetic resonance imaging (MRI) exam typically consists of several sequences that yield different image contrasts. Each sequence is parameterized through multiple acquisition parameters that influence image contrast, signal-to-noise ratio, acquisition time, and/or resolution. Depending on the clinical indication, different contrasts are required by the radiologist to make a diagnosis. As MR sequence acquisition is time consuming and acquired images may be corrupted due to motion, a method to synthesize MR images with adjustable contrast properties is required. Methods: Therefore, we trained an image-to-image generative adversarial network conditioned on the MR acquisition parameters repetition time and echo time. Our approach is motivated by style transfer networks, whereas the “style” for an image is explicitly given in our case, as it is determined by the MR acquisition parameters our network is conditioned on. Results: This enables us to synthesize MR images with adjustable image contrast. We evaluated our approach on the fastMRI dataset, a large set of publicly available MR knee images, and show that our method outperforms a benchmark pix2pix approach in the translation of non-fat-saturated MR images to fat-saturated images. Our approach yields a peak signal-to-noise ratio and structural similarity of 24.48 and 0.66, surpassing the pix2pix benchmark model significantly. Conclusion: Our model is the first that enables fine-tuned contrast synthesis, which can be used to synthesize missing MR-contrasts or as a data augmentation technique for AI training in MRI. It can also be used as basis for other image-to-image translation tasks within medical imaging, e.g., to enhance intermodality translation (MRI → CT) or 7 T image synthesis from 3 T MR images.
CRIS-Team Scopus Importer:2021-07-02
Keywords: Deep learning; Generative adversarial networks; Image synthesis; Magnetic resonance imaging

MR-projection imaging with perspective distortion as in X-ray fluoroscopy for interventional X/MR-hybrid applications - Lommen, Jonathan; Syben-Leisner, Christopher; Stimpel, Bernhard; Bayer, Siming; Nagel, Armin Michael; Fahrig, Rebecca; Dörfler, Arnd; Maier, Andreas (MR-projection imaging with perspective distortion as in X-ray fluoroscopy for interventional X/MR-hybrid applications, 2018)

MR to X-ray Projection Image Synthesis - Stimpel, Bernhard; Syben-Leisner, Christopher; Würfl, Tobias; Mentl, Katrin; Dörfler, Arnd; Maier, Andreas (Proceedings of the 5th International Conference on Image Formation in X-ray Computed Tomography (CT-Meeting), 2018)
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.mrtoxr_0

MR-zero meets RARE MRI: Joint optimization of refocusing flip angles and neural networks to minimize T2-induced blurring in spin echo sequences - Dang, Hoai Nam; Endres, Jonathan; Weinmüller, Simon; Glang, Felix; Loktyushin, Alexander; Scheffler, Klaus; Dörfler, Arnd; Schmidt, Manuel; Maier, Andreas; Zaiß, Moritz (Magnetic Resonance in Medicine, 2023)
Purpose: An end-to-end differentiable 2D Bloch simulation is used to reduce T2 induced blurring in single-shot turbo spin echo sequences, also called rapid imaging with refocused echoes (RARE) sequences, by using a joint optimization of refocusing flip angles and a convolutional neural network. Methods: Simulation and optimization were performed in the MR-zero framework. Variable flip angle train and DenseNet parameters were optimized jointly using the instantaneous transverse magnetization, available in our simulation, at a certain echo time, which serves as ideal blurring-free target. Final optimized sequences were exported for in vivo measurements at a real system (3 T Siemens, PRISMA) using the Pulseq standard. Results: The optimized RARE was able to successfully lower T2-induced blurring for single-shot RARE sequences in proton density-weighted and T2-weighted images. In addition to an increased sharpness, the neural network allowed correction of the contrast changes to match the theoretical transversal magnetization. The optimization found flip angle design strategies similar to existing literature, however, visual inspection of the images and evaluation of the respective point spread function demonstrated an improved performance. Conclusions: This work demonstrates that when variable flip angles and a convolutional neural network are optimized jointly in an end-to-end approach, sequences with more efficient minimization of T2-induced blurring can be found. This allows faster single- or multi-shot RARE MRI with longer echo trains.
CRIS-Team Scopus Importer:2023-07-07
Keywords: DenseNet; joint-optimization; MR-zero; RARE; RF-pulse design; T2-blurring; TSE

Multi-Channel Volumetric Neural Network for Knee Cartilage Segmentation in Cone-Beam CT - Maier, Jennifer; Rivera Monroy, Luis; Syben-Leisner, Christopher; Jeon, Yejin; Choi, Jang-Hwan; Hall, Mary; Levenston, Marc; Gold, Garry; Fahrig, Rebecca; Maier, Andreas (Bildverarbeitung für die Medizin 2020, 2020)
Analyzing knee cartilage thickness and strain under load can help to further the understanding of the effects of diseases like Osteoarthritis. A precise segmentation of the cartilage is a necessary prerequisite for this analysis. This segmentation task has mainly been addressed in Magnetic Resonance Imaging, and was rarely investigated on contrast-enhanced Computed Tomography, where contrast agent visualizes the border between femoral and tibial cartilage. To overcome the main drawback of manual segmentation, namely its high time investment, we propose to use a 3D Convolutional Neural Network for this task. The presented architecture consists of a V-Net with SeLu activation, and a Tversky loss function. Due to the high imbalance between very few cartilage pixels and many background pixels, a high false positive rate is to be expected. To reduce this rate, the two largest segmented point clouds are extracted using a connected component analysis, since they most likely represent the medial and lateral tibial cartilage surfaces. The resulting segmentations are compared to manual segmentations, and achieve on average a recall of 0.69, which confirms the feasibility of this approach.<br

Multi-Dimensional Flow-Preserving Compressed Sensing (MuFloCoS) for Time-Resolved Velocity-Encoded Phase Contrast MRI - Hutter, Jana; Schmitt, Peter; Saake, Marc; Stübinger, Axel; Grimm, Robert; Forman, Christoph; Greiser, Andreas; Hornegger, Joachim; Maier, Andreas (IEEE Transactions on Medical Imaging, 2015)
4-D time-resolved velocity-encoded phase-contrast MRI (4-D PCI) is a fully non-invasive technique to assess hemodynamics in vivo with a broad range of potential applications in multiple cardiovascular diseases. It is capable of providing quantitative flow values and anatomical information simultaneously. The long acquisition time, however, still inhibits its wider clinical use. Acceleration is achieved at present using parallel MRI (pMRI) techniques which can lead to substantial loss of image quality for higher acceleration factors. Both the high-dimensionality and the significant degree of spatio-temporal correlation in 4-D PCI render it ideally suited for recently proposed compressed sensing (CS) techniques. We propose the Multi-Dimensional Flow-preserving Compressed Sensing (MuFloCoS) method to exploit these properties. A multi-dimensional iterative reconstruction is combined with an interleaved sampling pattern (I-VT), an adaptive masked and weighted temporal regularization (TMW) and fully automatically obtained vessel-masks. The performance of the novel method was analyzed concerning image quality, feasibility of acceleration factors up to 15, quantitative flow values and diagnostic accuracy in phantom experiments and an in vivo carotid study with 18 volunteers. Comparison with iterative state-of-the-art methods revealed significant improvements using the new method, the temporal normalized root mean square error of the peak velocity was reduced by 45.32% for the novel MuFloCoS method with acceleration factor 9. The method was furthermore applied to two patient cases with diagnosed high-grade stenosis of the ICA, which confirmed the performance of MuFloCoS to produce valuable results in the presence of pathological findings in 56 s instead of over 8 min (full sampling).
UnivIS-Import:2015-07-08:Pub.2015.tech.IMMD.IMMD5.multid
Keywords: Compressed sensing; hemodynamics; magnetic resonance imaging (MRI); phase contrast MRI

Multidimensional noise reduction in C-arm cone-beam CT via 2D-based Landweber iteration and 3D-based deep neural networks - Choi, Dahim; Kim, Juhee; Chae, Seung Hoon; Kim, Byeongjoon; Baek, Jongduk; Maier, Andreas; Fahrig, Rebecca; Park, Hyun Seok; Choi, Jang Hwan (Progress in Biomedical Optics and Imaging - Proceedings of SPIE, 2019)
Recently, the necessity of using low-dose CT imaging with reduced noise has come to the forefront due to the risks involved in radiation. In order to acquire a high-resolution image from a low-resolution image which produces a relatively small amount of radiation, various algorithms including deep learning-based methods have been proposed. However, the current techniques have shown limited performance, especially with regard to losing fine details and blurring high-frequency edges. To enhance the previously suggested 2D patch-based denoising model, we have suggested the 3D block-based REDCNN model, employing convolution layers paired with deconvolution layers, shortcuts, and residual mappings. This process allows us to preserve the image structure and diagnostic features of an image, increasing image resolution by smoothing noise. Finally, we applied a bilateral filter in 3D and utilized a 2D-based Landweber iteration method to reduce remaining noise under a certain amplitude and prevent the edges from blurring. As a result, our proposed method effectively reduced Poisson noise level without losing diagnostic features and showed high performance in both qualitative and quantitative evaluation methods compared to ResNet2D, ResNet3D, REDCNN2D, and REDCNN3D.
CRIS-Team Scopus Importer:2019-07-12
Keywords: Bilateral filtering; C-arm cone-beam CT; Deep neural networks; Imaging of lower extremities; Landweber type iteration; Medical image processing; Noise reduction

Multi-level cancer profiling through joint cell-graph representations - Rivera Monroy, Luis; Rist, Leonhard; Wilm, Frauke; Ostalecki, Christian; Baur, Andreas; Vera González, Julio; Breininger, Katharina; Maier, Andreas (Smart Health, 2024)
Computer-aided analysis of digitized pathology samples has significantly advanced with the rapid progression of machine and Deep Learning (DL) methods. However, most existing approaches primarily focus on features extracted from patches due to the large image sizes. This focus limits the ability of Convolutional Neural Networks (CNNs) to capture global information from the samples, resulting in an incomplete phenotypical and topological representation and thereby restricting the diagnostic capabilities of these methods. The recent emergence of Graph Neural Networks (GNNs) offers new opportunities to overcome these limitations through graph-driven representations of pathological samples. This work introduces a graph-based framework that encompasses diverse cancer types and integrates different imaging modalities. In this framework, histopathology samples are represented as graphs, and a pipeline facilitating cell-wise and disease classification is developed. The results support this motivation: for cell-wise classification, we achieved an average accuracy of , and for disease-wise classification, an average accuracy of 83%, outperforming reference models such as XGBoost and standard CNNs. This approach not only provides flexibility in combining various diseases but also extends to integrating different staining technique
Keywords: Fluorescence microscopy imageGraph deep learningMultiplex imagingDigital pathology

Multi-Material Beam Hardening Correction (MMBHC) in Computed Tomography - Yang, Qiao; Maass, Nicole; Tian, Mengqiu; Elter, Matthias; Schasiepen, Ingo; Maier, Andreas; Hornegger, Joachim (Proceedings of the 12th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2013)

Multi-Modal Biomarker Extraction Framework for Therapy Monitoring of Social Anxiety and Depression Using Audio and Video - Yang, Seung Hee; Pérez Toro, Paula Andrea; Weise, Tobias; Hoffmann, Bettina; Demir, Kubilay; Nöth, Elmar; Kallert, Thomas; Maier, Andreas (International Conference on Machine Learning (Workshop on Machine Learning for Multimodal Healthcare), 2023)
This paper introduces a framework that can be used for feature extraction, relevant to monitoring the speech therapy progress of individuals suffering from social anxiety or depression. It operates multi-modal (decision fusion) by incorporating audio and video recordings of a patient and the corresponding interviewer, at two separate test assessment sessions. The used data is provided by an ongoing project in a day-hospital and outpatient setting in Germany, with the goal of investigating whether an established speech therapy group program for adolescents, which is implemented in a stationary and semi-stationary setting, can be successfully carried out via telemedicine. The features proposed in this multi-modal approach could form the basis for interpretation and analysis by medical experts and therapists, in addition to acquired data in the form of questionnaires. Extracted audio features focus on prosody (intonation, stress, rhythm, and timing), as well as predictions from a deep neural network model, which is inspired by the Pleasure, Arousal, Dominance (PAD) emotional model space. Video features are based on a pipeline that is designed to enable visualization of the interaction between the patient and the interviewer in terms of Facial Emotion Recognition (FER), utilizing the mini-Xception network architecture.<br

Multi-Modal Deep Guided Filtering for Comprehensible Medical Image Processing Supplementary Material - Stimpel, Bernhard; Syben-Leisner, Christopher; Schirrmacher, Franziska; Hölter, Philip; Dörfler, Arnd; Maier, Andreas (IEEE Transactions on Medical Imaging, 2019)
EVALuna2:212806

Multi-modal Retinal Image Registration Using a Keypoint-Based Vessel Structure Aligning Network - Sindel, Aline; Hohberger, Bettina; Maier, Andreas; Christlein, Vincent (Medical Image Computing and Computer Assisted Intervention – MICCAI 2022, 2022)
In ophthalmological imaging, multiple imaging systems, such as color fundus, infrared, fluorescein angiography, optical coherence tomography (OCT) or OCT angiography, are often involved to make a diagnosis of retinal disease. Multi-modal retinal registration techniques can assist ophthalmologists by providing a pixel-based comparison of aligned vessel structures in images from different modalities or acquisition times. To this end, we propose an end-to-end trainable deep learning method for multi-modal retinal image registration. Our method extracts convolutional features from the vessel structure for keypoint detection and description and uses a graph neural network for feature matching. The keypoint detection and description network and graph neural network are jointly trained in a self-supervised manner using synthetic multi-modal image pairs and are guided by synthetically sampled ground truth homographies. Our method demonstrates higher registration accuracy as competing methods for our synthetic retinal dataset and generalizes well for our real macula dataset and a public fundus dataset.
Keywords: Multi-modal retinal image registration; Convolutional neural networks; Graph neural networks

Multi-Modal Super-Resolution with Deep Guided Filtering - Stimpel, Bernhard; Syben-Leisner, Christopher; Schirrmacher, Franziska; Dörfler, Arnd; Maier, Andreas; Hölter, Philip (Informatik aktuell, 2019)
Despite the visually appealing results, most Deep Learningbased super-resolution approaches lack the comprehensibility that is required for medical applications. We propose a modified version of the locally linear guided filter for the application of super-resolution in medical imaging. The guidance map itself is learned end-to-end from multimodal inputs, while the actual data is only processed with known operators. This ensures comprehensibility of the results and simplifies the implementation of guarantees. We demonstrate the possibilities of our approach based on multi-modal MR and cross-modal CT and MR data. For both datasets, our approach performs clearly better than bicubic upsampling. For projection images, we achieve SSIMs of up to 0.99, while slice image data results in SSIMs of up to 0.98 for four-fold upsampling given an image of the respective other modality at full resolution. In addition, end-to-end learning of the guidance map considerably improves the quality of the results.
CRIS-Team Scopus Importer:2019-05-14

Multi-organ Segmentation with Partially Annotated Datasets - Song, Haobo; Liu, Chang; Folle, Lukas; Maier, Andreas (Bildverarbeitung für die Medizin 2022, 2022)
Efficient and fully automatic multi-organ segmentation is of great research and clinical prospect. Deep learning (DL) based methods have recently emerged and proven its effectiveness in various biomedical segmentation tasks. The performance of DL based segmentation models strongly depends on the training dataset and a large, correctly annotated dataset is always crucial. However, gathering annotation for multi-organ segmentation task is difficult and making use of public datasets with existing annotations then becomes one possible solution. In this work we propose a pipeline for training multi-organ segmentation model from partially annotated datasets. The proposed method is evaluated using left, right lungs and liver segmentation task of throat-abdomen CT scans. From average dice score, we found the proposed method can obtain very close performance using only partially annotated datasets (0.93), compared with models using fully annotated datasets (0.96).<br

Multiple Device Segmentation for Fluoroscopic Imaging Using Multi-task Learning - Breininger, Katharina; Würfl, Tobias; Kurzendorfer, Tanja; Albarqouni, Shadi; Pfister, Marcus; Kowarschik, Markus; Navab, Nassir; Maier, Andreas (Intravascular Imaging and Computer Assisted Stenting and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, 2018)
For endovascular aortic repair (EVAR), integrating preoperative information of the aortic anatomy with intraoperative fluoroscopy can aid in reducing radiation exposure, contrast agent and procedure time. However, the quality of this fusion may deteriorate over the course of the intervention due to patient movement or deformation of the vasculature caused by interventional tools. Automatically detecting the instruments present in the X-ray image can help to assess the degree of deterioration, trigger automatic re-registration or aid in automatic workflow phase detection and process modeling. In this work, we investigate a flexible approach to segment different devices based on fully convolutional neural networks using multi-task learning. We evaluate the proposed approach on a set of 38 X-ray images acquired during EVAR interventions by targeting the segmentation of aortic stents, stiff guidewires and pigtail catheters. We compare the results to the performance of single-task networks. We manage to keep similar performance compared to single-task networks with Dice coefficients between 0.95 and 0.80 depending on the device, while speeding up computation by a factor of two.
UnivIS-Import:2019-08-15:Pub.2018.tech.IMMD.IMMD5.multip
Keywords: Catheters; Convolutional Neural Networks; Endovascular Aortic Repair; Guidewires; Multi-task learning; Stents; X-ray

Multiple Source Translation Micro-CT Improves Both Field-of-View and Spatial Resolution - Yu, Haijun; Ni, Song; Thies, Mareike; Wagner, Fabian; Huang, Yixing; Liu, Fenglin; Maier, Andreas (IEEE Transactions on Instrumentation and Measurement, 2024)
Keywords: Data sampling; field-of-view (FOV); micro-computed tomography (micro-CT); scanning mode; spatial resolution.

Multi-Scale Deep Reinforcement Learning for Real-Time 3D-Landmark Detection in CT Scans - Ghesu, Florin-Cristian; Georgescu, Bogdan; Zheng, Yefeng; Grbic, Sasa; Maier, Andreas; Hornegger, Joachim; Comaniciu, Dorin (IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019)
Robust and fast detection of anatomical structures is a prerequisite for both diagnostic and interventional medical image analysis. Current solutions for anatomy detection are typically based on machine learning techniques that exploit large annotated image databases in order to learn the appearance of the captured anatomy. These solutions are subject to several limitations, including the use of suboptimal feature engineering techniques and most importantly the use of computationally suboptimal search-schemes for anatomy detection. To address these issues, we propose a method that follows a new paradigm by reformulating the detection problem as a behavior learning task for an artificial agent. We couple the modeling of the anatomy appearance and the object search in a unified behavioral framework, using the capabilities of deep reinforcement learning and multi-scale image analysis. In other words, an artificial agent is trained not only to distinguish the target anatomical object from the rest of the body but also how to find the object by learning and following an optimal navigation path to the target object in the imaged volumetric space. We evaluated our approach on 1487 3D-CT volumes from 532 patients, totaling over 500,000 image slices and show that it significantly outperforms state-of-the-art solutions on detecting several anatomical structures with no failed cases from a clinical acceptance perspective, while also achieving a 20-30 percent higher detection accuracy. Most importantly, we improve the detection-speed of the reference methods by 2-3 orders of magnitude, achieving unmatched real-time performance on large 3D-CT scans.
CRIS-Team Scopus Importer:2019-06-07
Keywords: Deep learning; deep reinforcement learning; intelligent localization; medical image analysis; multi-scale; real-time detection; scale-space modeling; three-dimensional (3D) object detection

Multi-scanner Canine Cutaneous Squamous Cell Carcinoma Histopathology Dataset - Wilm, Frauke; Fragoso, Marco; Bertram, Christof A.; Stathonikos, Nikolas; Öttl, Mathias; Qiu, Jingna; Klopfleisch, Robert; Maier, Andreas; Breininger, Katharina; Aubreville, Marc (Informatik aktuell, 2023)
In histopathology, scanner-induced domain shifts are known to impede the performance of trained neural networks when tested on unseen data. Multidomain pre-training or dedicated domain-generalization techniques can help to develop domain-agnostic algorithms. For this, multi-scanner datasets with a high variety of slide scanning systems are highly desirable. We present a publicly available multi-scanner dataset of canine cutaneous squamous cell carcinoma histopathology images, composed of 44 samples digitized with five slide scanners. This dataset provides local correspondences between images and thereby isolates the scanner-induced domain shift from other inherent, e.g. morphology-induced domain shifts. To highlight scanner differences, we present a detailed evaluation of color distributions, sharpness, and contrast of the individual scanner subsets. Additionally, to quantify the inherent scanner-induced domain shift, we train a tumor segmentation network on each scanner subset and evaluate the performance both in - and cross-domain. We achieve a class-averaged in-domain intersection over union coefficient of up to 0.86 and observe a cross-domain performance decrease of up to 0.38, which confirms the inherent domain shift of the presented dataset and its negative impact on the performance of deep neural networks.
Created from Fastlane, Scopus look-up

Multi-Stage Platform for (Semi-)Automatic Planning in Reconstructive Orthopedic Surgery - Kordon, Florian Johannes; Maier, Andreas; Swartman, Benedict; Privalov, Maxim; El Barbari, Jan Siad; Kunze, Holger (Journal of Imaging, 2022)
Intricate lesions of the musculoskeletal system require reconstructive orthopedic surgery to restore the correct biomechanics. Careful pre-operative planning of the surgical steps on 2D image data is an essential tool to increase the precision and safety of these operations. However, the plan’s effectiveness in the intra-operative workflow is challenged by unpredictable patient and device positioning and complex registration protocols. Here, we develop and analyze a multi-stage algorithm that combines deep learning-based anatomical feature detection and geometric post-processing to enable accurate pre- and intra-operative surgery planning on 2D X-ray images. The algorithm allows granular control over each element of the planning geometry, enabling real-time adjustments directly in the operating room (OR). In the method evaluation of three ligament reconstruction tasks effect on the knee joint, we found high spatial precision in drilling point localization (ε<2.9mm" role="presentation" style="max-height: none; display: inline; line-height: normal; word-spacing: normal; overflow-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; min-width: 0px; min-height: 0px; border-width: 0px; border-style: initial; position: relative;">ε<2.9mm) and low angulation errors for k-wire instrumentation (ε<0.75∘" role="presentation" style="max-height: none; display: inline; line-height: normal; word-spacing: normal; overflow-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; min-width: 0px; min-height: 0px; border-width: 0px; border-style: initial; position: relative;">ε<0.75∘) on 38 diagnostic radiographs. Comparable precision was demonstrated in 15 complex intra-operative trauma cases suffering from strong implant overlap and multi-anatomy exposure. Furthermore, we found that the diverse feature detection tasks can be efficiently solved with a multi-task network topology, improving precision over the single-task case. Our platform will help overcome the limitations of current clinical practice and foster surgical plan generation and adjustment directly in the OR, ultimately motivating the development of novel 2D planning guidelines.<br
Keywords: computer-assisted surgery; surgical planning; reconstructive orthopedic surgery; ligament reconstruction; deep learning; multi-task learning; X-ray images

Multi-Task Framework for X-Ray Guided Planning in Knee Surgery - Kordon, Florian Johannes; Fischer, Peter; Privalov, Maxim; Swartman, Benedict; Schnetzke, Marc; Franke, Jochen; Lasowski, Ruxandra; Maier, Andreas; Kunze, Holger (Bildverarbeitung für die Medizin 2020, 2020)
Keywords: Landmark Localization; Multi-label Bone Segmentation; MPFL X-Ray Guidance; Orthopaedics; Surgical Planning

Multi-task Learning for Chest X-ray Abnormality Classification on Noisy Labels - Gündel, Sebastian; Ghesu, Florin-Cristian; Grbic, Sasa; Gibson, Eli; Georgescu, Bogdan; Maier, Andreas; Comaniciu, Dorin (2019)

Multi-task Localization and Segmentation for X-Ray Guided Planning in Knee Surgery - Kordon, Florian Johannes; Fischer, Peter; Privalov, Maxim; Swartman, Benedict; Schnetzke, Marc; Franke, Jochen; Lasowski, Ruxandra; Maier, Andreas; Kunze, Holger (Medical Image Computing and Computer Assisted Intervention – MICCAI 2019, 2019)
X-ray based measurement and guidance are commonly used tools in orthopaedic surgery to facilitate a minimally invasive workflow. Typically, a surgical planning is first performed using knowledge of bone morphology and anatomical landmarks. Information about bone location then serves as a prior for registration during overlay of the planning on intra-operative X-ray images. Performing these steps manually however is prone to intra-rater/inter-rater variability and increases task complexity for the surgeon. To remedy these issues, we propose an automatic framework for planning and subsequent overlay. We evaluate it on the example of femoral drill site planning for medial patellofemoral ligament reconstruction surgery. A deep multi-task stacked hourglass network is trained on 149 conventional lateral X-ray images to jointly localize two femoral landmarks, to predict a region of interest for the posterior femoral cortex tangent line, and to perform semantic segmentation of the femur, patella, tibia, and fibula with adaptive task complexity weighting. On 38 clinical test images the framework achieves a median localization error of 1.50 mm for the femoral drill site and mean IOU scores of 0.99, 0.97, 0.98, and 0.96 for the femur, patella, tibia, and fibula respectively. The demonstrated approach consistently performs surgical planning at expert-level precision without the need for manual correctio
Keywords: Landmark localization; Multi-label bone segmentation; MPFL X-ray guidance; Orthopaedics; Surgical planning

Multi-View Depth-Aware Rigid 2-D/3-D Registration - Schaffert, Roman; Wang, Jian; Fischer, Peter; Borsdorf, Anja; Maier, Andreas (2017 IEEE Nuclear Science Symposium and Medical Imaging Conference Record (NSS/MIC), 2017)
UnivIS-Import:2018-02-22:Pub.2017.tech.IMMD.IMMD5.multiv

Myocardial Scar Segmentation in LGE-MRI using Fractal Analysis and Random Forest Classification - Kurzendorfer, Tanja; Breininger, Katharina; Steidl, Stefan; Brost, Alexander; Forman, Christoph; Maier, Andreas (Proceedings - International Conference on Pattern Recognition, 2018)
Late-gadolinium enhanced magnetic resonance imaging (LGE-MRI) is the clinical gold standard to visualize myocardial scarring. The gadolinium based contrast agent accumulates in the damaged cells and leads to various enhancements in the LGE-MRI scan. The quantification of the scar tissue is very important for diagnosis, treatment planning, and guidance during the procedure. In clinical routine, the scar is often segmented manually. However, manual segmentation is prone to inter- and intra-observer variability and very time consuming. In this work a new texture based scar quantification is proposed. For texture characterization, segmentation based fractal analysis is used. First, the image is decomposed into a set of binary images by applying a two-threshold binary decomposition. Second, a set of features are extracted for each of the binary images, namely the fractal dimension, the mean gray value, and the size of the binary object. In addition, the local and global intensity of each patch is added to the feature vector. In the next step, the features are classified using a random forest classifier. The scar quantification is evaluated on 30 clinical LGE-MRI data sets. In addition, the results are compared to the x-fold standard deviation approach and the full-width-at-half-max method, which are implemented in a fully automatic manner. The proposed scar quantification achieved a mean Dice coefficient of 0.64±0.17 and outperforms the x-fold standard deviation approach.
CRIS-Team Scopus Importer:2020-11-20

Myocardial scar segmentation on cardiac LGE images with reduced contrast agent dose using deep learning - Vornehm, Marc; Fenski, Maximilian; Preuhs, Elisabeth; Maier, Andreas; Schulz-Menger, Jeanette; Wetzl, Jens; Giese, Daniel (Abstractband der 24. Jahrestagung der Deutschen Sektion der ISMRM, 2022)

Myocardial Twist from X-ray Angiography - Geimer, Tobias; Unberath, Mathias; Höhn, Johannes; Achenbach, Stephan; Maier, Andreas (Bildverarbeitung für die Medizin 2018 - Algorithmen - Systeme - Anwendungen, 2018)
We present preliminary evidence that left ventricular twist can be observed and thus estimated from rotational coronary angiography. Our method is based on an ellipsoidal parametric model initially developed for functional analysis of cardiac tagged MRI. First, we fit the model to 3D coronary artery centerlines reconstructed from rotational angiography and then use 3D/2D registration to optimize for the functional parameters driving the model. On two clinical acquisitions, we show that our method is able to recover cardiac motion indicated by an average reduction in reprojection error of 28.1±3.0 %. Analysis of the functional progression of the functional parameters over time reveals radial and longitudinal contraction, and left ventricular twist. We believe that these results are exciting and encourage improvement of the proposed method in future work.<br
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.myocar

Myocardial twist from X-ray angiography: Can we observe left ventricular twist in rotational coronary angiography? - Geimer, Tobias; Unberath, Mathias; Höhn, Johannes; Achenbach, Stephan; Maier, Andreas (Informatik aktuell, 2018)
We present preliminary evidence that left ventricular twist can be observed and thus estimated from rotational coronary angiography. Our method is based on an ellipsoidal parametric model initially developed for functional analysis of cardiac tagged MRI. First, we fit the model to 3D coronary artery centerlines reconstructed from rotational angiography and then use 3D/2D registration to optimize for the functional parameters driving the model. On two clinical acquisitions, we show that our method is able to recover cardiac motion indicated by an average reduction in reprojection error of 28.1±3.0%. Analysis of the functional progression of the functional parameters over time reveals radial and longitudinal contraction, and left ventricular twist. We believe that these results are exciting and encourage improvement of the proposed method in future work.
CRIS-Team Scopus Importer:2022-06-05

Neural network based formation of cognitive maps of semantic spaces and the putative emergence of abstract concepts - Stöwer, Paul; Schilling, Achim; Maier, Andreas; Krauß, Patrick (Scientific Reports, 2023)

Neural network based successor representations to form cognitive maps - Stöwer, Paul; Krauß, Patrick; Maier, Andreas (Bernstein Conference, 2022)

Neural network based successor representations to form cognitive maps of space and language - Stöwer, Paul; Schliecker, Christian; Schilling, Achim; Metzner, Claus; Maier, Andreas; Krauß, Patrick (Scientific Reports, 2022)

Neural Networks for Distinguishing Rheumatoid Arthritis from Psoriatic Arthritis by Using Magnetic Resonance Imaging - Folle, Lukas; Bayat, Sara; Kleyer, Arnd; Fagni, Filippo; Kapsner, Lorenz; Schlereth, Maja; Meinderink, Timo; Breininger, Katharina; Tascilar, Koray; Krönke, Gerhard; Uder, Michael; Sticherling, Michael; et al. (Arthritis Rheumatol., 2022)

Neural Networks with Fixed Binary Random Projections Improve Accuracy in Classifying Noisy Data - Yang, Zijin; Schilling, Achim; Maier, Andreas; Krauß, Patrick (Informatik aktuell, 2021)
CRIS-Team Scopus Importer:2021-04-19

New Approaches to Dose Reduction in Interventional CT Imaging - Maier, Andreas (International Hospital, 2013)
UnivIS-Import:2016-02-10:Pub.2013.tech.IMMD.IMMD5.newapp

New Approaches to Dose Reduction in Interventional CT Imaging - Maier, Andreas (2013)

Next-generation imaging of the skeletal system and its blood supply - Grüneboom, Anika; Kling, Lasse; Christiansen, Silke; Mill, Leonid; Maier, Andreas; Engelke, Klaus; Quick, Harald H.; Schett, Georg; Gunzer, Matthias (Nature Reviews Rheumatology, 2019)
Bone is organized in a hierarchical 3D architecture. Traditionally, analysis of the skeletal system was based on bone mass assessment by radiographic methods or on the examination of bone structure by 2D histological sections. Advanced imaging technologies and big data analysis now enable the unprecedented examination of bone and provide new insights into its 3D macrostructure and microstructure. These technologies comprise ex vivo and in vivo methods including high-resolution computed tomography (CT), synchrotron-based imaging, X-ray microscopy, ultra-high-field magnetic resonance imaging (MRI), light-sheet fluorescence microscopy, confocal and intravital two-photon imaging. In concert, these techniques have been used to detect and quantify a novel vascular system of trans-cortical vessels in bone. Furthermore, structures such as the lacunar network, which harbours and connects osteocytes, become accessible for 3D imaging and quantification using these methods. Next-generation imaging of the skeletal system and its blood supply are anticipated to contribute to an entirely new understanding of bone tissue composition and function, from macroscale to nanoscale, in health and disease. These insights could provide the basis for early detection and precision-type intervention of bone disorders in the future.
CRIS-Team Scopus Importer:2019-08-27

Noise2Contrast: Multi-contrast Fusion Enables Self-supervised Tomographic Image Denoising - Wagner, Fabian; Thies, Mareike; Pfaff, Laura; Maul, Noah; Pechmann, Sabrina; Gu, Mingxuan; Utz, Jonas; Aust, Oliver; Weidner, Daniela; Neag, Georgiana; Uderhardt, Stefan; Choi, Jang Hwan; et al. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2023)
Self-supervised image denoising techniques emerged as convenient methods that allow training denoising models without requiring ground-truth noise-free data. Existing methods usually optimize loss metrics that are calculated from multiple noisy realizations of similar images, e.g., from neighboring tomographic slices. However, those approaches fail to utilize the multiple contrasts that are routinely acquired in medical imaging modalities like MRI or dual-energy CT. In this work, we propose the new self-supervised training scheme Noise2Contrast that combines information from multiple measured image contrasts to train a denoising model. We stack denoising with domain-transfer operators to utilize the independent noise realizations of different image contrasts to derive a self-supervised loss. The trained denoising operator achieves convincing quantitative and qualitative results, outperforming state-of-the-art self-supervised methods by 4.7–11.0%/4.8–7.3% (PSNR/SSIM) on brain MRI data and by 43.6–50.5%/57.1–77.1% (PSNR/SSIM) on dual-energy CT X-ray microscopy data with respect to the noisy baseline. Our experiments on different real measured data sets indicate that Noise2Contrast training generalizes to other multi-contrast imaging modalities.
CRIS-Team Scopus Importer:2023-07-28
Keywords: Contrast Fusion; Known Operator Learning; Self-Supervised Denoising

Noise Reduction In Low-Dose CT Using a 3D Multiscale Sparse Denoising Autoencoder - Mentl, Katrin; Mailhé, Boris; Ghesu, Florin-Cristian; Schebesch, Frank; Haderlein, Tino; Maier, Andreas; Nadar, Mariappan (2017 IEEE INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING, 2017)
UnivIS-Import:2017-12-18:Pub.2017.tech.IMMD.IMMD5.noiser_1

Noise reduction in optical coherence tomography images using a deep neural network with perceptually-sensitive loss function - Qiu, Bin; Huang, Zhiyu; Liu, Xi; Meng, Xiangxi; You, Yunfei; Liu, Gangjun; Yang, Kun; Maier, Andreas; Ren, Qiushi; Lu, Yanye (Biomedical Optics Express, 2020)
Optical coherence tomography (OCT) is susceptible to the coherent noise, which is the speckle noise that deteriorates contrast and the detail structural information of OCT images, thus imposing significant limitations on the diagnostic capability of OCT. In this paper, we propose a novel OCT image denoising method by using an end-to-end deep learning network with a perceptually-sensitive loss function. The method has been validated on OCT images acquired from healthy volunteers’ eyes. The label images for training and evaluating OCT denoising deep learning models are images generated by averaging 50 frames of respective registered B-scans acquired from a region with scans occurring in one direction. The results showed that the new approach can outperform other related denoising methods on the aspects of preserving detail structure information of retinal layers and improving the perceptual metrics in the human visual perception.
CRIS-Team Scopus Importer:2020-02-14

Noise reduction method in low-dose CT data combining neural networks and an iterative reconstruction technique - Choi, Dahim; Kim, Juhee; Chae, Seung Hoon; Baek, Jongduk; Maier, Andreas; Fahrig, Rebecca; Park, Hyun Seok; Choi, Jang Hwan (Proceedings of SPIE - The International Society for Optical Engineering, 2019)
Improving image quality from low-dose CT image and keeping diagnostic features is integral to lowering the amount of exposure to radiation and its potential risks. Noise reduction methods using deep neural network have been developed and displayed impressive performance, but there are limitations on noise remnants, blurring on high-frequency edge, and artifacts occurrence. To increase noise reduction performance and deal with those issues simultaneously, we have implemented block-based REDCNN model and applied patch-based Landweber-type iteration to images passed through REDCNN model. The model successfully smooths noise on CT images which are imposed Gaussian and Poisson noise, and outperforms noise reduction by other state-of-the-art deep neural network models. We also have tested the effect of repetition of an iterative reconstruction, changing a step size and the number of iteration.
CRIS-Team Scopus Importer:2019-05-02
Keywords: Deep Neural Networks; Landweber iteration; Low-dose CT; Noise Reduction; REDCNN

Non-Contrast Head CT Imaging Using Dual Energy: A Study on Bias Induced by Imperfections in Energy Response and Material Knowledge - Haase, Viktor; Stierstorfer, Karl; Hahn, Katharina; Schoendube, Harald; Maier, Andreas; Noo, Frédéric (Proceedings of the 16th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine (Fully3D), 2021)
Diagnostic of stroke and proton therapy treatment planning require accurate non-contrast head CT imaging. Dual energy CT offers improvements in this field by enabling material decomposition and virtual mono-energetic images. Such a computation in the projection space requires an energy response model for the CT system and a selection of materials for data decomposition. In this work, we use incorrect response models and materials to study the effects of imperfections on the quality of the material decomposition. The experiments are carried out in fanbeam geometry with noise-free computer simulated data of two head phantoms. The results show that small errors in the energy response can quickly lead to small inhomogeneity and to beam hardening errors between close bones. They also show that the difference between soft tissues and water lead to soft tissue leakage in the bone image, but may not have a negative effect on the mono-energetic image.<br
Keywords: Dual Energy Computed Tomography; Material Decomposition; Energy Response Model; Non-Contrast Head CT Imaging; Simulation Study

Non-Destructive Digitization of Soiled Historical Chinese Bamboo Scrolls - Stromer, Daniel; Christlein, Vincent; Huang, Xiaolin; Zippert, Patrick; Helmecke, Eric; Hausotte, Tino; Maier, Andreas (2018 13th IAPR Workshop on Document Analysis Systems (DAS), 2018)
For about 2000 years, no paper was used as a media in China but writings and drawings were captured on bamboo and wooden slips. Several slips were bound together with strips and rolled up to a scroll. The writings and drawings were either brushed or even carved into the wood. Those documents are very precious for culture inheritance and research, but due to aging processes, the discovered pieces are sometimes in a poor condition and also soiled. Because cleaning the slips is not only challenging but also writings could be erased, we developed a method to digitize such historical documents without the need of cleaning. We perform a 3-D X-ray micro-CT scan resulting in a 3-D volume of the complete document. With our approach, we were able to investigate the scroll without any manual labor (e.g. unwrapping or cleaning). We showed that the method also works for heavily soiled scrolls where nothing is readable with the naked eye. This can help conservators to store all writings before they may be erased by the cleaning process. Finally, we present a manual technique to virtually unwrap and post-process the documents resulting in a 2-D image of all bamboo slips.
UnivIS-Import:2018-09-06:Pub.2018.tech.IMMD.IMMD5.nondes
Keywords: Bamboo and wooden slips; 3-D X-ray CT; Historical Chinese Documents; Document Digitization

Non-deterministic Behavior of Ranking-Based Metrics When Evaluating Embeddings - Nicolaou, Anguelos; Dey, Sounak; Christlein, Vincent; Maier, Andreas; Karatzas, Dimosthenis (RRPR 2018: Reproducible Research in Pattern Recognition, 2019)
Embedding data into vector spaces is a very popular strategy of pattern recognition methods. When distances between embeddings are quantized, performance metrics become ambiguous. In this paper, we present an analysis of the ambiguity quantized distances introduce and provide bounds on the effect. We demonstrate that it can have a measurable effect in empirical data in state-of-the-art systems. We also approach the phenomenon from a computer security perspective and demonstrate how someone being evaluated by a third party can exploit this ambiguity and greatly outperform a random predictor without even access to the input data. We also suggest a simple solution making the performance metrics, which rely on ranking, totally deterministic and impervious to such exploits.
CRIS-Team Scopus Importer:2019-07-23
Keywords: Adversarial; Deterministic; mAP; Performance evaluation; Precision; Ranking; Recall; Word spotting

Normalization techniques for CNN based analysis of surgical cone beam CT volumes - Martín Vicario, Celia; Kordon, Florian Johannes; Denzinger, Felix; El Barbari, Jan Siad; Privalov, Maxim; Franke, Jochen; Maier, Andreas; Kunze, Holger (Medial Imaging 2022, Image Processing, 2022)
Purpose: The choice of input normalization has effects on the generalization and performance of deep neural networks. While this topic is explored for 2D imaging applications, the influence of different normalization techniques on medical imaging modalities, e.g. cone-beam CT (CBCT), differs due to a different value range and distribution. In this paper a good normalization technique for intra-operatively acquired surgical CBCT volumes is presented. Methods: A set of normalization strategies, namely histogram equalization, min-max scaling, z-score normalization, linear look up table (LUT) with clipping and sigmoid function with clipping is compared on a CBCT volume classification task. Results: The results show that a combination of parameterized LUTs and clipping with the range [-710, 1640] HU independent of the underlying intensity histogram provides the best performance for the task at hand. Conclusions: The clipping based normalization technique helps to compress the feature space to the relevant range. By this approach, most of the information about the intensity values of soft tissue and bone is retained. The clipping range presented in this paper is valid for surgical CBCTs.<br
Keywords: Cone-beam CT; Normalization

Novel Evaluation Metrics for Vascular Structure Segmentation - Reimann, Marcel; Fu, Weilin; Maier, Andreas (Informatik aktuell, 2021)
CRIS-Team Scopus Importer:2021-04-09

Numeric quantification of intelligibility in schoolchildren with isolated and combined cleft palate - Vogt, Beate; Maier, Andreas; Batliner, Anton; Nöth, Elmar; Nkenke, Emeka; Eysholdt, Ulrich; Schuster, Maria (HNO, 2007)
Objective. Even after adequate surgical and nonsurgical treatment, combined or isolated clefts often cause functional disorders, such as speech disorders. Speech disorders vary widely in extent and can take the form, for example, of specific features of articulation, with altered nasal emission and shifted articulation, leading to reduced speech intelligibility. So far it has not been possible to describe the relationship between cleft type and intelligibility except subjective, categorical evaluation. Methods. Intelligibility of the speech as influenced by cleft palate in 58 children aged 9±2 was quantified objectively and numerically by means of automatic speech recognition technology in speech recordings for the PLAKSS test [test for psycholinguistic analysis of childhood speech impairments]. It was found that 33 children had a unilateral cleft lip and palate (CLP), 9, bilateral CLP, 10, isolated cleft palate, and 6, submucosal cleft palate. As a control group, 84 children aged 9±1 years and with physiological articulation were recruited from a mainstream school. Speech intelligibility is expressed as word accuracy, which means the percentage of correctly recognized words in a word sequence. Results. Word accuracy levels in the control group were between 39% and 84% (mean 62%±10%), while the children with clefts achieved values between 0% and 73% (mean 42%±17%). There was no difference in intelligibility between children with isolated cleft palate and those with combined cleft type. Differences were, however, found between each group and the control group (p<0.01). Conclusion. Speech intelligibility of children with isolated cleft palate does not significantly differ from that of children with combined cleft type including the palate, as has been objectively quantified for the first time by objective methods. In the presence of speech disorders, the need for diagnosis and therapy is just as great in children with isolated cleft palate as in children with combined cleft types. © 2007 Springer Medizin Verlag.
Keywords: Automatic speech recognition; Cleft lip and palate; Isolated cleft palate; Objective evaluation; Speech intelligibility

Numerische Quantifizierung der Verständlichkeit von Schulkindern mit isolierter und kombinierter Gaumenspalte - Vogt, Beate; Maier, Andreas; Batliner, Anton; Nöth, Elmar; Nkenke, Emeka; Eysholdt, Ulrich; Schuster, Maria (HNO, 2007)

Objective vs. Subjective Evaluation of Speakers with and without Complete Dentures - Haderlein, Tino; Bocklet, Tobias; Maier, Andreas; Nöth, Elmar; Knipfer, Christian; Stelzle, Florian (Proc. Text, Speech and Dialogue; 12th International Conference, 2009)

Object Removal in Gradient Domain of Cone-Beam CT Projections - Bier, Bastian; Berger, Martin; Maier, Jennifer; Unberath, Mathias; Hsieh, Scott; Bonaretti, Serena; Fahrig, Rebecca; Levenston, Marc; Gold, Garry; Maier, Andreas (2016 IEEE Nuclear Science Symposuim & Medical Imaging Conference Record, 2016)

Objektive und automatische Ermittlung der Verständlichkeit von Kindern und Jugendlichen mit Lippen-Kiefer-Gaumenspalten - Schuster, Maria; Maier, Andreas; Vogt, Beate; Nöth, Elmar; Nkenke, Emeka; Holst, Alexandra; Eysholdt, Ulrich; Rosanowski, Frank (Aktuelle phoniatrisch-pädaudiologische Aspekte 2006, 2006)

OCPAD - Occluded Checkerboard Pattern Detector - Fürsattel, Peter; Deitsch, Sergiu; Placht, Simon; Balda, Michael; Maier, Andreas; Riess, Christian (IEEE Winter Conference on Applications of Computer Vision, 2016)
Many camera calibration techniques require the detection of a pattern with known geometry, e.g., a checkerboard. Typically, the pattern must be fully contained in the field of view. This brings several limitations, one of which is that lens distortion can not reliably be estimated in outer image regions. This paper presents the occluded checkerboard pattern detector (OCPAD) to find checkerboards, even in a) low-resolution images, b) images with high lens distortion and if c) the pattern is partly occluded or not completely within the field of view. We exploit that checkerboards can easily be represented by a graph. We use graph matching to find the largest partial checkerboard in the image. Our detector complements a state-of-the-art calibration algorithm. Quantitatively, detection rates are considerably improved over the state-of-the-art. Additionally, estimation of lens distortion is greatly improved at outer image regions. Here, the reprojection error is improved by up to 50%.<br

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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