TY - CHAP A1 - Sengupta, Agniva A1 - Kuş, Dilara A1 - Li, Jianning A1 - Zachow, Stefan T1 - Globally Optimal Pose from Orthographic Silhouettes N2 - We solve the problem of determining the pose of known shapes in R^3 from their unoccluded silhouettes. The pose is determined up to global optimality using a simple yet under-explored property of the area-of-silhouette: its continuity w.r.t trajectories in the rotation space. The proposed method utilises pre-computed silhouette-signatures, modelled as a response surface of the area-of-silhouettes. Querying this silhouette-signature response surface for pose estimation leads to a strong branching of the rotation search space, making resolution-guided candidate search feasible. Additionally, we utilise the aspect ratio of 2D ellipses fitted to projected silhouettes as an auxiliary global shape signature to accelerate the pose search. This combined strategy forms the first method to efficiently estimate globally optimal pose from just the silhouettes, without being guided by correspondences, for any shape, irrespective of its convexity and genus. We validate our method on synthetic and real examples, demonstrating significantly improved accuracy against comparable approaches. Y1 - 2026 ER - TY - JOUR A1 - Trepczynski, Adam A1 - Kneifel, Paul A1 - Heyland, Mark A1 - Leskovar, Marko A1 - Moewis, Philippe A1 - Damm, Philipp A1 - Taylor, William R. A1 - Zachow, Stefan A1 - Duda, Georg N. T1 - Impact of the external knee flexion moment on patello-femoral loading derived from in vivo loads and kinematics JF - Frontiers in Bioengineering and Biotechnology N2 - Introduction: Anterior knee pain and other patello-femoral (PF) complications frequently limit the success of total knee arthroplasty as the final treatment of end stage osteoarthritis. However, knowledge about the in-vivo loading conditions at the PF joint remains limited, as no direct measurements are available. We hypothesised that the external knee flexion moment (EFM) is highly predictive of the PF contact forces during activities with substantial flexion of the loaded knee. Materials and methods: Six patients (65–80 years, 67–101 kg) with total knee arthroplasty (TKA) performed two activities of daily living: sit-stand-sit and squat. Tibio-femoral (TF) contact forces were measured in vivo using instrumented tibial components, while synchronously internal TF and PF kinematics were captured with mobile fluoroscopy. The measurements were used to compute PF contact forces using patient specific musculoskeletal models. The relationship between the EFM and the PF contact force was quantified using linear regression. Results: Mean peak TF contact forces of 1.97–3.24 times body weight (BW) were found while peak PF forces reached 1.75 to 3.29 times body weight (BW). The peak EFM ranged from 3.2 to 5.9 %BW times body height, and was a good predictor of the PF contact force (R2 = 0.95 and 0.88 for sit-stand-sit and squat, respectively). Discussion: The novel combination of in vivo TF contact forces and internal patellar kinematics enabled a reliable assessment of PF contact forces. The results of the regression analysis suggest that PF forces can be estimated based solely on the EFM from quantitative gait analysis. Our study also demonstrates the relevance of PF contact forces, which reach magnitudes similar to TF forces during activities of daily living. Y1 - 2024 U6 - https://doi.org/10.3389/fbioe.2024.1473951 SN - 2296-4185 VL - 12/2024 PB - Frontiers Media SA ER - TY - CHAP A1 - Manogue, Kevin A1 - Schang, Tomasz A1 - Kuş, Dilara A1 - Müller, Jonas A1 - Zachow, Stefan A1 - Sengupta, Agniva T1 - Generalizing Shape-from-Template to Topological Changes T2 - Smart Tools and Applications in Graphics - Eurographics Italian Chapter Conference N2 - Reconstructing the surfaces of deformable objects from correspondences between a 3D template and a 2D image is well studied under Shape-from-Template (SfT) methods; however, existing approaches break down when topological changes accompany the deformation. We propose a principled extension of SfT that enables reconstruction in the presence of such changes. Our approach is initialized with a classical SfT solution and iteratively adapts the template by partitioning its spatial domain so as to minimize an energy functional that jointly encodes physical plausibility and reprojection consistency. We demonstrate that the method robustly captures a wide range of practically relevant topological events including tears and cuts on bounded 2D surfaces, thereby establishing the first general framework for topological-change-aware SfT. Experiments on both synthetic and real data confirm that our approach consistently outperforms baseline methods. Y1 - 2025 SN - 978-3-03868-296-7 U6 - https://doi.org/10.2312/stag.20251322 PB - The Eurographics Association ER - TY - JOUR A1 - Li, Jianning A1 - Bitter, Kerstin A1 - Nguyen, Anh Duc A1 - Shemesh, Hagay A1 - Zaslansky, Paul A1 - Zachow, Stefan T1 - Computational Insights into Root Canal Treatment: A Survey of Selected Methods in Imaging, Segmentation, Morphological Analysis, and Clinical Management JF - dentistry journal N2 - Background/Objectives: Root canal treatment (RCT) is a common dental procedure performed to preserve teeth by removing infected or at-risk pulp tissue caused by caries, trauma, or other pulpal conditions. A successful outcome, among others, depends on accurate identification of the root canal anatomy, planning a suitable therapeutic strategy, and ensuring a bacteria-tight root canal filling. Despite advances in dental techniques, there remains limited integration of computational methods to support key stages of treatment. This review aims to provide a comprehensive overview of computational methods applied throughout the full workflow of RCT, examining their potential to support clinical decision-making, improve treatment planning and outcome assessment, and help bridge the interdisciplinary gap between dentistry and computational research. Methods: A comprehensive literature review was conducted to identify and analyze computational methods applied to different stages of RCT, including root canal segmentation, morphological analysis, treatment planning, quality evaluation, follow-up, and prognosis prediction. In addition, a taxonomy based on application was developed to categorize these methods based on their function within the treatment process. Insights from the authors’ own research experience were also incorporated to highlight implementation challenges and practical considerations. Results: The review identified a wide range of computational methods aimed at enhancing the consistency and efficiency of RCT. Key findings include the use of advanced image processing for segmentation, image analysis for diagnosis and treatment planning, machine learning for morphological classification, and predictive modeling for outcome estimation. While some methods demonstrate high sensitivity and specificity in diagnostic and planning tasks, many remain in experimental stages and lack clinical integration. There is also a noticeable absence of advanced computational techniques for micro-computed tomography and morphological analysis. Conclusions: Computational methods offer significant potential to improve decision-making and outcomes in RCT. However, greater focus on clinical translation and development of cross-modality methodology is needed. The proposed taxonomy provides a structured framework for organizing existing methods and identifying future research directions tailored to specific phases of treatment. This review serves as a resource for both dental professionals, computer scientists and researchers seeking to bridge the gap between clinical practice and computational innovation. Y1 - 2025 U6 - https://doi.org/https://doi.org/10.3390/dj13120579 VL - 13 IS - 12 PB - MDPI ER - TY - JOUR A1 - Leskovar, Marko A1 - Heyland, Mark A1 - Trepczynski, Adam A1 - Zachow, Stefan T1 - Comparison of Global and Local Optimization Methods for Intensity-based 2D-3D Registration JF - Computers in Biology and Medicine Y1 - 2025 U6 - https://doi.org/10.1016/j.compbiomed.2024.109574 VL - 186 SP - 109574 PB - Elsevier ER - TY - CHAP A1 - Siqueira Rodrigues, Lucas A1 - Kosch, Thomas A1 - Nyakatura, John A1 - Zachow, Stefan A1 - Israel, Johann Habakuk T1 - MorphoHaptics: An Open-Source Tool for Visuohaptic Exploration of Morphological Image Datasets T2 - KUI '24: Proceedings of the 21th International Conference on Culture and Computer Science: from Humanism to Digital Humanities N2 - Although digital methods have significantly advanced morphology, practitioners are still challenged to understand and process tomographic data of specimens. As automated processing of fossil data is still insufficient, morphologists still engage in intensive manual work to digitally prepare fossils for research objectives. We present an open-source tool that enables morphologists to explore tomographic data similarly to the physical workflows that traditional fossil preparators experience in the field. Using questionnaires, we assessed the usability of our prototype for virtual fossil preparation and related common tasks in the digital preparation workflow. Our findings indicate that integrating haptics into the virtual preparation workflow enhances the understanding of the morphology and material properties of working specimens and that the visuohaptic sculpting of fossil volumes is straightforward and is an improvement over current digital specimen processing methods. Y1 - 2025 U6 - https://doi.org/10.1145/3719236.3719271 SP - 1 EP - 10 ER - TY - JOUR A1 - Sengupta, Agniva A1 - Zachow, Stefan T1 - Shape-from-Template with Generalised Camera JF - Image and Vision Computing N2 - This article presents a new method for non-rigidly registering a 3D shape to 2D keypoints observed by a constellation of multiple cameras. Non-rigid registration of a 3D shape to observed 2D keypoints, i.e., Shape-from-Template (SfT), has been widely studied using single images, but SfT with information from multiple-cameras jointly opens new directions for extending the scope of known use-cases such as 3D shape registration in medical imaging and registration from hand-held cameras, to name a few. We represent such multi-camera setup with the generalised camera model; therefore any collection of perspective or orthographic cameras observing any deforming object can be registered. We propose multiple approaches for such SfT: the first approach where the corresponded keypoints lie on a direction vector from a known 3D point in space, the second approach where the corresponded keypoints lie on a direction vector from an unknown 3D point in space but with known orientation w.r.t some local reference frame, and a third approach where, apart from correspondences, the silhouette of the imaged object is also known. Together, these form the first set of solutions to the SfT problem with generalised cameras. The key idea behind SfT with generalised camera is the improved reconstruction accuracy from estimating deformed shape while utilising the additional information from the mutual constraints between multiple views of a deformed object. The correspondence-based approaches are solved with convex programming while the silhouette-based approach is an iterative refinement of the results from the convex solutions. We demonstrate the accuracy of our proposed methods on many synthetic and real data. Y1 - 2025 U6 - https://doi.org/10.1016/j.imavis.2025.105579 VL - 162 ER - TY - JOUR A1 - Deppe, Dominik A1 - Gabriele, Matteo A1 - Mazzoleni, Manuel Giovanni A1 - Ordas-Bayon, Alejandro A1 - Fidanza, Andrea A1 - Rozhko, Yuriy A1 - Şimşek, Ekin Kaya A1 - Keltz, Eran A1 - Osterhoff, Georg A1 - Damm, Philipp A1 - Duda, Georg N. A1 - Leskovar, Marko A1 - Zachow, Stefan A1 - Trepczynski, Adam A1 - Heyland, Mark T1 - Interobserver Reliability of the Modified Radiographic Union Score (mRUST) for Tibial and Femoral Fractures JF - Journal of Orthopaedic Trauma N2 - OBJECTIVES: To evaluate the reliability of the modified Radiographic Union Score for Tibial fractures (mRUST) as a reliable tool for monitoring lower limb fractures (femur, tibia) treated with various modalities (nail, plate). METHODS: Design: Retrospective analysis. Setting: Single center academic hospital in Germany. Patient Selection Criteria: Adult patients (≥18 years) with extra-articular long bone fractures of the lower extremities treated surgically between January 2005 and April 2022, requiring radiographs in two perpendicular planes and at least one follow-up visit, were included. Exclusion criteria were critical clinical conditions, inability to consent, joint articulation fractures, inadequate documentation, or insufficient imaging quality. Outcome Measures and Comparisons: Six international investigators (five orthopedic surgeons, one radiologist) independently assessed fracture line and callus growth per cortex (mRUST) at individualized follow-up time points based on clinical practice. To evaluate interrater reliability, intraclass correlation coefficients were calculated for the overall dataset, and for subsets of rated images, that were defined based on anatomical location (femur/tibia), treatment type (plate/nail fixation), and treatment combinations across locations. RESULTS: A total of 166 patients (63 femur fractures, 103 tibia fractures; 32.5% female, mean age 43.4 (18–84)) with 1136 follow-up time points were analyzed. Overall interrater reliability for mRUST was good (intraclass correlation coefficient 0.77), consistent across fixation methods (nail/plate fixation, 0.79) and anatomical locations (tibia, 0.78; femur, 0.81). Cortex-specific reliability varied, with highest agreement for the medial cortex (0.70–0.74) and lowest for the posterior cortex (0.65–0.74). CONCLUSIONS: The mRUST (radiographic score) demonstrated reliability for monitoring fracture healing in the femur and tibia, irrespective of fixation method, supporting its use as a generalizable tool across lower limb fractures. LEVEL OF EVIDENCE: Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence. Y1 - 2025 U6 - https://doi.org/10.1097/BOT.0000000000003032 SN - 0890-5339 VL - 39 IS - 10 SP - 557 EP - 563 ER - TY - JOUR A1 - Xie, Kunpeng A1 - Gruber, Lennart Johannes A1 - Crampen, Martin A1 - Li, Yao A1 - Ferreira, André A1 - Tappeiner, Elias A1 - Gillot, Maxime A1 - Schepers, Jan A1 - Xu, Jiangchang A1 - Pankert, Tobias A1 - Beyer, Michel A1 - Shahamiri, Negar A1 - ten Brink, Reinier A1 - Dot, Gauthier A1 - Weschke, Charlotte A1 - van Nistelrooij, Niels A1 - Verhelst, Pieter-Jan A1 - Guo, Yan A1 - Xu, Zhibin A1 - Bienzeisler, Jonas A1 - Rashad, Ashkan A1 - Flügge, Tabea A1 - Cotton, Ross A1 - Vinayahalingam, Shankeeth A1 - Ilesan, Robert A1 - Raith, Stefan A1 - Madsen, Dennis A1 - Seibold, Constantin A1 - Xi, Tong A1 - Bergé, Stefaan A1 - Nebelung, Sven A1 - Kodym, Oldřich A1 - Sundqvist, Osku A1 - Thieringer, Florian A1 - Lamecker, Hans A1 - Coppens, Antoine A1 - Potrusil, Thomas A1 - Kraeima, Joep A1 - Witjes, Max A1 - Wu, Guomin A1 - Chen, Xiaojun A1 - Lambrechts, Adriaan A1 - Cevidanes, Lucia H Soares A1 - Zachow, Stefan A1 - Hermans, Alexander A1 - Truhn, Daniel A1 - Alves, Victor A1 - Egger, Jan A1 - Röhrig, Rainer A1 - Hölzle, Frank A1 - Puladi, Behrus T1 - Beyond Benchmarks: Towards Robust Artificial Intelligence Bone Segmentation in Socio-Technical Systems JF - Expert Systems With Applications N2 - Despite the advances in automated medical image segmentation, AI models still underperform in various clinical settings, challenging real-world integration. In this multicenter evaluation, we analyzed 20 state-of-the-art mandibular segmentation models across 19,218 segmentations of 1,000 clinically resampled CT/CBCT scans. We show that segmentation accuracy varies by up to 25% depending on socio-technical factors such as voxel size, bone orientation, and patient conditions such as osteosynthesis or pathology. Higher sharpness, isotropic smaller voxels, and neutral orientation significantly improved results, while metallic osteosynthesis and anatomical complexity led to significant degradation. Our findings challenge the common view of AI models as “plug-and-play” tools and suggest evidence-based optimization recommendations for both clinicians and developers. This will in turn boost the integration of AI segmentation tools in routine healthcare. Y1 - 2025 UR - https://www.medrxiv.org/content/10.1101/2025.06.11.25329022v1 U6 - https://doi.org/10.1016/j.eswa.2025.130031 VL - 299 IS - Part D ER - TY - JOUR A1 - Komnik, Igor A1 - Funken, Johannes A1 - Zachow, Stefan A1 - Schmidt-Wiethoff, Rüdiger A1 - Ellermann, Andree A1 - Potthast, Wolfgang T1 - Surgical planning in HTO – Alternative approaches to the Fujisawa gold-standard JF - Technology and Health Care N2 - BACKGROUND: Presurgical planning of the correction angle plays a decisive role in a high tibial osteotomy, affecting the loading situation in the knee affected by osteoarthritis. The planning approach by Fujisawa et al. aims to adjust the weight-bearing line to achieve an optimal knee joint load distribution. While this method is accessible, it may not fully consider the complexity of individual dynamic knee-loading profiles. This review aims to disclose existing alternative HTO planning methods that do not follow Fujisawa’s standard. METHODS: PubMed, Web of Science and CENTRAL databases were screened, focusing on HTO research in combination with alternative planning approaches. RESULTS: Eight out of 828 studies were included, with seven simulation studies based on finite element analysis and multi-body dynamics. The planning approaches incorporated gradual degrees of realignment parameters (weight-bearing line shift, medial proximal tibial angle, hip- knee-ankle, knee joint line orientation), simulating their effect on knee kinematics, contact force/stress, Von Mises and shear stress. Two studies proposed implementing individual correction magnitudes derived from preoperatively predicted knee adduction moments. CONCLUSION: Most planning methods depend on static alignment assessments, neglecting an adequate loading-depending profile. They are confined to their conceptual phases, making the associated planning methods unviable for current clinical use. Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-98227 ER - TY - JOUR A1 - Siqueira Rodrigues, Lucas A1 - Schmidt, Timo Torsten A1 - Israel, Johann Habakuk A1 - Nyakatura, John A1 - Zachow, Stefan A1 - Kosch, Thomas T1 - Comparing the Effects of Visual, Haptic, and Visuohaptic Encoding on Memory Retention of Digital Objects in Virtual Reality JF - NordiCHI '24: Proceedings of the 13th Nordic Conference on Human-Computer Interaction N2 - Although Virtual Reality (VR) has undoubtedly improved human interaction with 3D data, users still face difficulties retaining important details of complex digital objects in preparation for physical tasks. To address this issue, we evaluated the potential of visuohaptic integration to improve the memorability of virtual objects in immersive visualizations. In a user study (N=20), participants performed a delayed match-to-sample task where they memorized stimuli of visual, haptic, or visuohaptic encoding conditions. We assessed performance differences between the conditions through error rates and response time. We found that visuohaptic encoding significantly improved memorization accuracy compared to unimodal visual and haptic conditions. Our analysis indicates that integrating haptics into immersive visualizations enhances the memorability of digital objects. We discuss its implications for the optimal encoding design in VR applications that assist professionals who need to memorize and recall virtual objects in their daily work. Y1 - 2024 U6 - https://doi.org/10.1145/3679318.3685349 SP - 1 EP - 13 ER - TY - JOUR A1 - Siqueira Rodrigues, Lucas A1 - Schmidt, Timo Torsten A1 - Nyakatura, John A1 - Zachow, Stefan A1 - Israel, Johann Habakuk A1 - Kosch, Thomas T1 - Assessing the Effects of Sensory Modality Conditions on Object Retention across Virtual Reality and Projected Surface Display Environments JF - Proceedings of the ACM on Human-Computer Interaction N2 - Haptic feedback reportedly enhances human interaction with 3D data, particularly improving the retention of mental representations of digital objects in immersive settings. However, the effectiveness of visuohaptic integration in promoting object retention across different display environments remains underexplored. Our study extends previous research on the retention effects of haptics from virtual reality to a projected surface display to assess whether earlier findings generalize to 2D environments. Participants performed a delayed match-to-sample task incorporating visual, haptic, and visuohaptic sensory feedback within a projected surface display environment. We compared error rates and response times across these sensory modalities and display environments. Our results reveal that visuohaptic integration significantly enhances object retention on projected surfaces, benefiting task performance across display environments. Our findings suggest that haptics can improve object retention without requiring fully immersive setups, offering insights for the design of interactive systems that assist professionals who rely on precise mental representations of digital objects. Y1 - 2024 U6 - https://doi.org/10.1145/3698137 VL - 8 IS - ISS SP - 255 EP - 282 ER - TY - CHAP A1 - Siqueira Rodrigues, Lucas A1 - Nyakatura, John A1 - Zachow, Stefan A1 - Israel, Johann Habakuk A1 - Kosch, Thomas T1 - Evaluating Visuohaptic Integration on Memory Retention of Morphological Tomographic Images T2 - The 19th ACM SIGGRAPH International Conference on Virtual-Reality Continuum and its Applications in Industry N2 - Scientific visualization and tomographic imaging techniques have created unprecedented possibilities for non-destructive analyses of digital specimens in morphology. However, practitioners encounter difficulties retaining critical information from complex tomographic volumes in their workflows. In light of this challenge, we investigated the effectiveness of visuohaptic integration in enhancing memory retention of morphological data. In a within-subjects user study (N=18), participants completed a delayed match-to-sample task, where we compared error rates and response times across visual and visuohaptic sensory modality conditions. Our results indicate that visuohaptic encoding improves the retention of tomographic images, producing significantly reduced error rates and faster response times than its unimodal visual counterpart. Our findings suggest that integrating haptics into scientific visualization interfaces may support professionals in fields such as morphology, where accurate retention of complex spatial data is essential for efficient analysis and decision-making within virtual environments. Y1 - 2024 U6 - https://doi.org/10.1145/3703619.3706055 SP - 1 EP - 10 PB - ACM CY - New York, NY, USA ER - TY - JOUR A1 - Trepczynski, Adam A1 - Kneifel, Paul A1 - Heyland, Mark A1 - Leskovar, Marko A1 - Moewis, Philippe A1 - Damm, Philipp A1 - Taylor, William R. A1 - Zachow, Stefan A1 - Duda, Georg T1 - Impact of the External Knee Flexion Moment on Patello-Femoral Loading Derived from in vivo Loads and Kinematics JF - Frontiers in Bioengineering and Biotechnology N2 - Anterior knee pain and other patello-femoral (PF) complications frequently limit the success of total knee arthroplasty as the final treatment of end stage osteoarthritis. However, knowledge about the invivo loading conditions at the PF joint remains limited, as no direct measurements are available. We hypothesised that the external knee flexion moment (EFM) is highly predictive of the PF contact forces during activities with substantial flexion of the loaded knee.Six patients (65-80 years, 67-101 kg) with total knee arthroplasty (TKA) performed two activities of daily living: sit-stand-sit and squat. Tibio-femoral (TF) contact forces were measured in vivo using instrumented tibial components, while synchronously internal TF and PF kinematics were captured with mobile fluoroscopy. The measurements were used to compute PF contact forces using patient specific musculoskeletal models. The relationship between the EFM and the PF contact force was quantified using linear regression.Mean peak TF contact forces of 1.97 to 3.24 times body weight (BW) were found while peak PF forces reached 1.75 to 3.29 times body weight (BW). The peak EFM ranged from 3.2 to 5.9 %BW times body height, and was a good predictor of the PF contact force (R^2 = 0.95 and 0.88 for sit-standsit and squat, respectively).The novel combination of in vivo TF contact forces and internal patellar kinematics enabled a reliable assessment of PF contact forces. The results of the regression analysis suggest that PF forces can be estimated based solely on the EFM from quantitative gait analysis. Our study also demonstrates the relevance of PF contact forces, which reach magnitudes similar to TF forces during activities of daily living. Y1 - 2024 U6 - https://doi.org/10.3389/fbioe.2024.1473951 VL - 12 ER - TY - JOUR A1 - Amiranashvili, Tamaz A1 - Lüdke, David A1 - Li, Hongwei Bran A1 - Zachow, Stefan A1 - Menze, Bjoern T1 - Learning continuous shape priors from sparse data with neural implicit functions JF - Medical Image Analysis N2 - Statistical shape models are an essential tool for various tasks in medical image analysis, including shape generation, reconstruction and classification. Shape models are learned from a population of example shapes, which are typically obtained through segmentation of volumetric medical images. In clinical practice, highly anisotropic volumetric scans with large slice distances are prevalent, e.g., to reduce radiation exposure in CT or image acquisition time in MR imaging. For existing shape modeling approaches, the resolution of the emerging model is limited to the resolution of the training shapes. Therefore, any missing information between slices prohibits existing methods from learning a high-resolution shape prior. We propose a novel shape modeling approach that can be trained on sparse, binary segmentation masks with large slice distances. This is achieved through employing continuous shape representations based on neural implicit functions. After training, our model can reconstruct shapes from various sparse inputs at high target resolutions beyond the resolution of individual training examples. We successfully reconstruct high-resolution shapes from as few as three orthogonal slices. Furthermore, our shape model allows us to embed various sparse segmentation masks into a common, low-dimensional latent space — independent of the acquisition direction, resolution, spacing, and field of view. We show that the emerging latent representation discriminates between healthy and pathological shapes, even when provided with sparse segmentation masks. Lastly, we qualitatively demonstrate that the emerging latent space is smooth and captures characteristic modes of shape variation. We evaluate our shape model on two anatomical structures: the lumbar vertebra and the distal femur, both from publicly available datasets. Y1 - 2024 U6 - https://doi.org/10.1016/j.media.2024.103099 VL - 94 SP - 103099 ER - TY - CHAP A1 - Siqueira Rodrigues, Lucas A1 - Riehm, Felix A1 - Zachow, Stefan A1 - Israel, Johann Habakuk T1 - VoxSculpt: An Open-Source Voxel Library for Tomographic Volume Sculpting in Virtual Reality T2 - 2023 9th International Conference on Virtual Reality (ICVR), Xianyang, China, 2023 N2 - Manual processing of tomographic data volumes, such as interactive image segmentation in medicine or paleontology, is considered a time-consuming and cumbersome endeavor. Immersive volume sculpting stands as a potential solution to improve its efficiency and intuitiveness. However, current open-source software solutions do not yield the required performance and functionalities. We address this issue by contributing a novel open-source game engine voxel library that supports real-time immersive volume sculpting. Our design leverages GPU instancing, parallel computing, and a chunk-based data structure to optimize collision detection and rendering. We have implemented features that enable fast voxel interaction and improve precision. Our benchmark evaluation indicates that our implementation offers a significant improvement over the state-of-the-art and can render and modify millions of visible voxels while maintaining stable performance for real-time interaction in virtual reality. Y1 - 2023 U6 - https://doi.org/10.1109/ICVR57957.2023.10169420 SP - 515 EP - 523 ER - TY - CHAP A1 - Siqueira Rodrigues, Lucas A1 - Nyakatura, John A1 - Zachow, Stefan A1 - Israel, Johann Habakuk T1 - Design Challenges and Opportunities of Fossil Preparation Tools and Methods T2 - Proceedings of the 20th International Conference on Culture and Computer Science: Code and Materiality N2 - Fossil preparation is the activity of processing paleontological specimens for research and exhibition purposes. In addition to traditional mechanical extraction of fossils, preparation presently comprises non-destructive digital methods that are part of a relatively new field, namely virtual paleontology. Despite significant technological advances, both traditional and digital preparation remain cumbersome and time-consuming endeavors. However, this field has received scarce attention from a human-computer interaction perspective. The present study aims to elucidate the state-of-the-art for paleontological fossil preparation in order to determine its main challenges and start a conversation regarding opportunities for creating novel designs that tackle the field's current issues. We conducted a qualitative study involving both technical preparators and virtual paleontologists. The study was divided into two parts: First, we assembled technical preparators and paleontology researchers in a focus group session to discuss their workflows, obtain a preliminary understanding of their issues, and ideate solutions based on their counterparts' workflows. Next, we conducted a series of contextual inquiries involving direct observation and semi-structured in-depth interviews. We transcribed our recordings and examined the data through theoretical and inductive thematic analysis, clustering emerging themes and applying concepts from human-computer interaction and related fields. Our findings report on challenges faced by traditional and digital fossil preparators and potential opportunities to improve their tools and workflows. We contribute with a novel analysis of fossil preparation from an HCI perspective. Y1 - 2023 U6 - https://doi.org/10.1145/3623462.3623470 PB - Association for Computing Machinery CY - New York, NY, USA ER - TY - JOUR A1 - Wagendorf, Oliver A1 - Nahles, Susanne A1 - Vach, Kirstin A1 - Kernen, Florian A1 - Zachow, Stefan A1 - Heiland, Max A1 - Flügge, Tabea T1 - The impact of teeth and dental restorations on gray value distribution in cone-beam computer tomography - a pilot study JF - International Journal of Implant Dentistry N2 - Purpose: To investigate the influence of teeth and dental restorations on the facial skeleton's gray value distributions in cone-beam computed tomography (CBCT). Methods: Gray value selection for the upper and lower jaw segmentation was performed in 40 patients. In total, CBCT data of 20 maxillae and 20 mandibles, ten partial edentulous and ten fully edentulous in each jaw, respectively, were evaluated using two different gray value selection procedures: manual lower threshold selection and automated lower threshold selection. Two sample t tests, linear regression models, linear mixed models, and Pearson's correlation coefficients were computed to evaluate the influence of teeth, dental restorations, and threshold selection procedures on gray value distributions. Results: Manual threshold selection resulted in significantly different gray values in the fully and partially edentulous mandible. (p = 0.015, difference 123). In automated threshold selection, only tendencies to different gray values in fully edentulous compared to partially edentulous jaws were observed (difference: 58–75). Significantly different gray values were evaluated for threshold selection approaches, independent of the dental situation of the analyzed jaw. No significant correlation between the number of teeth and gray values was assessed, but a trend towards higher gray values in patients with more teeth was noted. Conclusions: Standard gray values derived from CT imaging do not apply for threshold-based bone segmentation in CBCT. Teeth influence gray values and segmentation results. Inaccurate bone segmentation may result in ill-fitting surgical guides produced on CBCT data and misinterpreting bone density, which is crucial for selecting surgical protocols. Y1 - 2023 U6 - https://doi.org/10.1186/s40729-023-00493-z VL - 9 IS - 27 ER - TY - JOUR A1 - Grewe, Carl Martin A1 - Liu, Tuo A1 - Hildebrandt, Andrea A1 - Zachow, Stefan T1 - The Open Virtual Mirror Framework for Enfacement Illusions - Enhancing the Sense of Agency With Avatars That Imitate Facial Expressions JF - Behavior Research Methods Y1 - 2022 U6 - https://doi.org/10.3758/s13428-021-01761-9 PB - Springer ER - TY - CHAP A1 - Siqueira Rodrigues, Lucas A1 - Nyakatura, John A1 - Zachow, Stefan A1 - Israel, Johann Habakuk T1 - An Immersive Virtual Paleontology Application T2 - 13th International Conference on Human Haptic Sensing and Touch Enabled Computer Applications, EuroHaptics 2022 N2 - Virtual paleontology studies digital fossils through data analysis and visualization systems. The discipline is growing in relevance for the evident advantages of non-destructive imaging techniques over traditional paleontological methods, and it has made significant advancements during the last few decades. However, virtual paleontology still faces a number of technological challenges, amongst which are interaction shortcomings of image segmentation applications. Whereas automated segmentation methods are seldom applicable to fossil datasets, manual exploration of these specimens is extremely time-consuming as it impractically delves into three-dimensional data through two-dimensional visualization and interaction means. This paper presents an application that employs virtual reality and haptics to virtual paleontology in order to evolve its interaction paradigms and address some of its limitations. We provide a brief overview of the challenges faced by virtual paleontology practitioners, a description of our immersive virtual paleontology prototype, and the results of a heuristic evaluation of our design. Y1 - 2022 U6 - https://doi.org/10.1007/978-3-031-06249-0 SP - 478 EP - 481 ER - TY - JOUR A1 - Glatzeder, Korbinian A1 - Komnik, Igor A1 - Ambellan, Felix A1 - Zachow, Stefan A1 - Potthast, Wolfgang T1 - Dynamic pressure analysis of novel interpositional knee spacer implants in 3D-printed human knee models JF - Scientific Reports N2 - Alternative treatment methods for knee osteoarthritis (OA) are in demand, to delay the young (< 50 Years) patient’s need for osteotomy or knee replacement. Novel interpositional knee spacers shape based on statistical shape model (SSM) approach and made of polyurethane (PU) were developed to present a minimally invasive method to treat medial OA in the knee. The implant should be supposed to reduce peak strains and pain, restore the stability of the knee, correct the malalignment of a varus knee and improve joint function and gait. Firstly, the spacers were tested in artificial knee models. It is assumed that by application of a spacer, a significant reduction in stress values and a significant increase in the contact area in the medial compartment of the knee will be registered. Biomechanical analysis of the effect of novel interpositional knee spacer implants on pressure distribution in 3D-printed knee model replicas: the primary purpose was the medial joint contact stress-related biomechanics. A secondary purpose was a better understanding of medial/lateral redistribution of joint loading. Six 3D printed knee models were reproduced from cadaveric leg computed tomography. Each of four spacer implants was tested in each knee geometry under realistic arthrokinematic dynamic loading conditions, to examine the pressure distribution in the knee joint. All spacers showed reduced mean stress values by 84–88% and peak stress values by 524–704% in the medial knee joint compartment compared to the non-spacer test condition. The contact area was enlarged by 462–627% as a result of the inserted spacers. Concerning the appreciable contact stress reduction and enlargement of the contact area in the medial knee joint compartment, the premises are in place for testing the implants directly on human knee cadavers to gain further insights into a possible tool for treating medial knee osteoarthritis. Y1 - 2022 U6 - https://doi.org/10.1038/s41598-022-20463-6 VL - 12 ER - TY - CHAP A1 - Amiranashvili, Tamaz A1 - Lüdke, David A1 - Li, Hongwei A1 - Menze, Bjoern A1 - Zachow, Stefan T1 - Learning Shape Reconstruction from Sparse Measurements with Neural Implicit Functions T2 - Medical Imaging with Deep Learning N2 - Reconstructing anatomical shapes from sparse or partial measurements relies on prior knowledge of shape variations that occur within a given population. Such shape priors are learned from example shapes, obtained by segmenting volumetric medical images. For existing models, the resolution of a learned shape prior is limited to the resolution of the training data. However, in clinical practice, volumetric images are often acquired with highly anisotropic voxel sizes, e.g. to reduce image acquisition time in MRI or radiation exposure in CT imaging. The missing shape information between the slices prohibits existing methods to learn a high-resolution shape prior. We introduce a method for high-resolution shape reconstruction from sparse measurements without relying on high-resolution ground truth for training. Our method is based on neural implicit shape representations and learns a continuous shape prior only from highly anisotropic segmentations. Furthermore, it is able to learn from shapes with a varying field of view and can reconstruct from various sparse input configurations. We demonstrate its effectiveness on two anatomical structures: vertebra and femur, and successfully reconstruct high-resolution shapes from sparse segmentations, using as few as three orthogonal slices. Y1 - 2022 ER - TY - CHAP A1 - Lüdke, David A1 - Amiranashvili, Tamaz A1 - Ambellan, Felix A1 - Ezhov, Ivan A1 - Menze, Bjoern A1 - Zachow, Stefan T1 - Landmark-free Statistical Shape Modeling via Neural Flow Deformations T2 - Medical Image Computing and Computer Assisted Intervention - MICCAI 2022 N2 - Statistical shape modeling aims at capturing shape variations of an anatomical structure that occur within a given population. Shape models are employed in many tasks, such as shape reconstruction and image segmentation, but also shape generation and classification. Existing shape priors either require dense correspondence between training examples or lack robustness and topological guarantees. We present FlowSSM, a novel shape modeling approach that learns shape variability without requiring dense correspondence between training instances. It relies on a hierarchy of continuous deformation flows, which are parametrized by a neural network. Our model outperforms state-of-the-art methods in providing an expressive and robust shape prior for distal femur and liver. We show that the emerging latent representation is discriminative by separating healthy from pathological shapes. Ultimately, we demonstrate its effectiveness on two shape reconstruction tasks from partial data. Our source code is publicly available (https://github.com/davecasp/flowssm). Y1 - 2022 U6 - https://doi.org/10.1007/978-3-031-16434-7_44 VL - 13432 PB - Springer, Cham ER - TY - CHAP A1 - Estacio, Laura A1 - Ehlke, Moritz A1 - Tack, Alexander A1 - Castro-Gutierrez, Eveling A1 - Lamecker, Hans A1 - Mora, Rensso A1 - Zachow, Stefan T1 - Unsupervised Detection of Disturbances in 2D Radiographs T2 - 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI) N2 - We present a method based on a generative model for detection of disturbances such as prosthesis, screws, zippers, and metals in 2D radiographs. The generative model is trained in an unsupervised fashion using clinical radiographs as well as simulated data, none of which contain disturbances. Our approach employs a latent space consistency loss which has the benefit of identifying similarities, and is enforced to reconstruct X-rays without disturbances. In order to detect images with disturbances, an anomaly score is computed also employing the Frechet distance between the input X-ray and the reconstructed one using our generative model. Validation was performed using clinical pelvis radiographs. We achieved an AUC of 0.77 and 0.83 with clinical and synthetic data, respectively. The results demonstrated a good accuracy of our method for detecting outliers as well as the advantage of utilizing synthetic data. Y1 - 2021 U6 - https://doi.org/10.1109/ISBI48211.2021.9434091 SP - 367 EP - 370 ER - TY - JOUR A1 - Sahu, Manish A1 - Mukhopadhyay, Anirban A1 - Zachow, Stefan T1 - Simulation-to-Real domain adaptation with teacher-student learning for endoscopic instrument segmentation JF - International Journal of Computer Assisted Radiology and Surgery N2 - Purpose Segmentation of surgical instruments in endoscopic video streams is essential for automated surgical scene understanding and process modeling. However, relying on fully supervised deep learning for this task is challenging because manual annotation occupies valuable time of the clinical experts. Methods We introduce a teacher–student learning approach that learns jointly from annotated simulation data and unlabeled real data to tackle the challenges in simulation-to-real unsupervised domain adaptation for endoscopic image segmentation. Results Empirical results on three datasets highlight the effectiveness of the proposed framework over current approaches for the endoscopic instrument segmentation task. Additionally, we provide analysis of major factors affecting the performance on all datasets to highlight the strengths and failure modes of our approach. Conclusions We show that our proposed approach can successfully exploit the unlabeled real endoscopic video frames and improve generalization performance over pure simulation-based training and the previous state-of-the-art. This takes us one step closer to effective segmentation of surgical instrument in the annotation scarce setting. Y1 - 2021 U6 - https://doi.org/10.1007/s11548-021-02383-4 N1 - Honorary Mention: Machine Learning for Computer-Assisted Intervention (CAI) Award @IPCAI2021 N1 - Honorary Mention: Audience Award for Best Innovation @IPCAI2021 VL - 16 SP - 849 EP - 859 PB - Springer Nature ER - TY - JOUR A1 - Tack, Alexander A1 - Preim, Bernhard A1 - Zachow, Stefan T1 - Fully automated Assessment of Knee Alignment from Full-Leg X-Rays employing a "YOLOv4 And Resnet Landmark regression Algorithm" (YARLA): Data from the Osteoarthritis Initiative JF - Computer Methods and Programs in Biomedicine N2 - We present a method for the quantification of knee alignment from full-leg X-Rays. A state-of-the-art object detector, YOLOv4, was trained to locate regions of interests (ROIs) in full-leg X-Ray images for the hip joint, the knee, and the ankle. Residual neural networks (ResNets) were trained to regress landmark coordinates for each ROI.Based on the detected landmarks the knee alignment, i.e., the hip-knee-ankle (HKA) angle, was computed. The accuracy of landmark detection was evaluated by a comparison to manually placed landmarks for 360 legs in 180 X-Rays. The accuracy of HKA angle computations was assessed on the basis of 2,943 X-Rays. Results of YARLA were compared to the results of two independent image reading studies(Cooke; Duryea) both publicly accessible via the Osteoarthritis Initiative. The agreement was evaluated using Spearman's Rho, and weighted kappa as well as regarding the correspondence of the class assignment (varus/neutral/valgus). The average difference between YARLA and manually placed landmarks was less than 2.0+- 1.5 mm for all structures (hip, knee, ankle). The average mismatch between HKA angle determinations of Cooke and Duryea was 0.09 +- 0.63°; YARLA resulted in a mismatch of 0.10 +- 0.74° compared to Cooke and of 0.18 +- 0.64° compared to Duryea. Cooke and Duryea agreed almost perfectly with respect to a weighted kappa value of 0.86, and showed an excellent reliability as measured by a Spearman's Rho value of 0.99. Similar values were achieved by YARLA, i.e., a weighted kappa value of0.83 and 0.87 and a Spearman's Rho value of 0.98 and 0.99 to Cooke and Duryea,respectively. Cooke and Duryea agreed in 92% of all class assignments and YARLA did so in 90% against Cooke and 92% against Duryea. In conclusion, YARLA achieved results comparable to those of human experts and thus provides a basis for an automated assessment of knee alignment in full-leg X-Rays. Y1 - 2021 U6 - https://doi.org/https://doi.org/10.1016/j.cmpb.2021.106080 VL - 205 IS - 106080 ER - TY - JOUR A1 - Ambellan, Felix A1 - Zachow, Stefan A1 - von Tycowicz, Christoph T1 - Rigid Motion Invariant Statistical Shape Modeling based on Discrete Fundamental Forms JF - Medical Image Analysis N2 - We present a novel approach for nonlinear statistical shape modeling that is invariant under Euclidean motion and thus alignment-free. By analyzing metric distortion and curvature of shapes as elements of Lie groups in a consistent Riemannian setting, we construct a framework that reliably handles large deformations. Due to the explicit character of Lie group operations, our non-Euclidean method is very efficient allowing for fast and numerically robust processing. This facilitates Riemannian analysis of large shape populations accessible through longitudinal and multi-site imaging studies providing increased statistical power. Additionally, as planar configurations form a submanifold in shape space, our representation allows for effective estimation of quasi-isometric surfaces flattenings. We evaluate the performance of our model w.r.t. shape-based classification of hippocampus and femur malformations due to Alzheimer's disease and osteoarthritis, respectively. In particular, we achieve state-of-the-art accuracies outperforming the standard Euclidean as well as a recent nonlinear approach especially in presence of sparse training data. To provide insight into the model's ability of capturing biological shape variability, we carry out an analysis of specificity and generalization ability. Y1 - 2021 U6 - https://doi.org/10.1016/j.media.2021.102178 VL - 73 ER - TY - GEN A1 - Grewe, C. Martin A1 - Zachow, Stefan T1 - Release of the FexMM for the Open Virtual Mirror Framework N2 - THIS MODEL IS FOR NON-COMMERCIAL RESEARCH PURPOSES. ONLY MEMBERS OF UNIVERSITIES OR NON-COMMERCIAL RESEARCH INSTITUTES ARE ELIGIBLE TO APPLY. 1. Download, fill, and sign the form available from: https://media.githubusercontent.com/media/mgrewe/ovmf/main/data/fexmm_license_agreement.pdf 2. Send the signed form to: fexmm@zib.de NOTE: Use an official email address of your institution for the request. Y1 - 2021 U6 - https://doi.org/10.12752/8532 ER - TY - JOUR A1 - Li, Jianning A1 - Pimentel, Pedro A1 - Szengel, Angelika A1 - Ehlke, Moritz A1 - Lamecker, Hans A1 - Zachow, Stefan A1 - Estacio, Laura A1 - Doenitz, Christian A1 - Ramm, Heiko A1 - Shi, Haochen A1 - Chen, Xiaojun A1 - Matzkin, Franco A1 - Newcombe, Virginia A1 - Ferrante, Enzo A1 - Jin, Yuan A1 - Ellis, David G. A1 - Aizenberg, Michele R. A1 - Kodym, Oldrich A1 - Spanel, Michal A1 - Herout, Adam A1 - Mainprize, James G. A1 - Fishman, Zachary A1 - Hardisty, Michael R. A1 - Bayat, Amirhossein A1 - Shit, Suprosanna A1 - Wang, Bomin A1 - Liu, Zhi A1 - Eder, Matthias A1 - Pepe, Antonio A1 - Gsaxner, Christina A1 - Alves, Victor A1 - Zefferer, Ulrike A1 - von Campe, Cord A1 - Pistracher, Karin A1 - Schäfer, Ute A1 - Schmalstieg, Dieter A1 - Menze, Bjoern H. A1 - Glocker, Ben A1 - Egger, Jan T1 - AutoImplant 2020 - First MICCAI Challenge on Automatic Cranial Implant Design JF - IEEE Transactions on Medical Imaging N2 - The aim of this paper is to provide a comprehensive overview of the MICCAI 2020 AutoImplant Challenge. The approaches and publications submitted and accepted within the challenge will be summarized and reported, highlighting common algorithmic trends and algorithmic diversity. Furthermore, the evaluation results will be presented, compared and discussed in regard to the challenge aim: seeking for low cost, fast and fully automated solutions for cranial implant design. Based on feedback from collaborating neurosurgeons, this paper concludes by stating open issues and post-challenge requirements for intra-operative use. Y1 - 2021 U6 - https://doi.org/10.1109/TMI.2021.3077047 SN - 0278-0062 VL - 40 IS - 9 SP - 2329 EP - 2342 ER - TY - JOUR A1 - Tack, Alexander A1 - Ambellan, Felix A1 - Zachow, Stefan T1 - Towards novel osteoarthritis biomarkers: Multi-criteria evaluation of 46,996 segmented knee MRI data from the Osteoarthritis Initiative JF - PLOS One N2 - Convolutional neural networks (CNNs) are the state-of-the-art for automated assessment of knee osteoarthritis (KOA) from medical image data. However, these methods lack interpretability, mainly focus on image texture, and cannot completely grasp the analyzed anatomies’ shapes. In this study we assess the informative value of quantitative features derived from segmentations in order to assess their potential as an alternative or extension to CNN-based approaches regarding multiple aspects of KOA. Six anatomical structures around the knee (femoral and tibial bones, femoral and tibial cartilages, and both menisci) are segmented in 46,996 MRI scans. Based on these segmentations, quantitative features are computed, i.e., measurements such as cartilage volume, meniscal extrusion and tibial coverage, as well as geometric features based on a statistical shape encoding of the anatomies. The feature quality is assessed by investigating their association to the Kellgren-Lawrence grade (KLG), joint space narrowing (JSN), incident KOA, and total knee replacement (TKR). Using gold standard labels from the Osteoarthritis Initiative database the balanced accuracy (BA), the area under the Receiver Operating Characteristic curve (AUC), and weighted kappa statistics are evaluated. Features based on shape encodings of femur, tibia, and menisci plus the performed measurements showed most potential as KOA biomarkers. Differentiation between non-arthritic and severely arthritic knees yielded BAs of up to 99%, 84% were achieved for diagnosis of early KOA. Weighted kappa values of 0.73, 0.72, and 0.78 were achieved for classification of the grade of medial JSN, lateral JSN, and KLG, respectively. The AUC was 0.61 and 0.76 for prediction of incident KOA and TKR within one year, respectively. Quantitative features from automated segmentations provide novel biomarkers for KLG and JSN classification and show potential for incident KOA and TKR prediction. The validity of these features should be further evaluated, especially as extensions of CNN- based approaches. To foster such developments we make all segmentations publicly available together with this publication. Y1 - 2021 U6 - https://doi.org/10.1371/journal.pone.0258855 VL - 16 IS - 10 ER - TY - JOUR A1 - Tack, Alexander A1 - Shestakov, Alexey A1 - Lüdke, David A1 - Zachow, Stefan T1 - A deep multi-task learning method for detection of meniscal tears in MRI data from the Osteoarthritis Initiative database JF - Frontiers in Bioengineering and Biotechnology, section Biomechanics N2 - We present a novel and computationally efficient method for the detection of meniscal tears in Magnetic Resonance Imaging (MRI) data. Our method is based on a Convolutional Neural Network (CNN) that operates on a complete 3D MRI scan. Our approach detects the presence of meniscal tears in three anatomical sub-regions (anterior horn, meniscal body, posterior horn) for both the Medial Meniscus (MM) and the Lateral Meniscus (LM) individually. For optimal performance of our method, we investigate how to preprocess the MRI data or how to train the CNN such that only relevant information within a Region of Interest (RoI) of the data volume is taken into account for meniscal tear detection. We propose meniscal tear detection combined with a bounding box regressor in a multi-task deep learning framework to let the CNN implicitly consider the corresponding RoIs of the menisci. We evaluate the accuracy of our CNN-based meniscal tear detection approach on 2,399 Double Echo Steady-State (DESS) MRI scans from the Osteoarthritis Initiative database. In addition, to show that our method is capable of generalizing to other MRI sequences, we also adapt our model to Intermediate-Weighted Turbo Spin-Echo (IW TSE) MRI scans. To judge the quality of our approaches, Receiver Operating Characteristic (ROC) curves and Area Under the Curve (AUC) values are evaluated for both MRI sequences. For the detection of tears in DESS MRI, our method reaches AUC values of 0.94, 0.93, 0.93 (anterior horn, body, posterior horn) in MM and 0.96, 0.94, 0.91 in LM. For the detection of tears in IW TSE MRI data, our method yields AUC values of 0.84, 0.88, 0.86 in MM and 0.95, 0.91, 0.90 in LM. In conclusion, the presented method achieves high accuracy for detecting meniscal tears in both DESS and IW TSE MRI data. Furthermore, our method can be easily trained and applied to other MRI sequences. Y1 - 2021 U6 - https://doi.org/10.3389/fbioe.2021.747217 SP - 28 EP - 41 ER - TY - GEN A1 - Tack, Alexander A1 - Shestakov, Alexey A1 - Lüdke, David A1 - Zachow, Stefan T1 - A deep multi-task learning method for detection of meniscal tears in MRI data from the Osteoarthritis Initiative database N2 - We present a novel and computationally efficient method for the detection of meniscal tears in Magnetic Resonance Imaging (MRI) data. Our method is based on a Convolutional Neural Network (CNN) that operates on a complete 3D MRI scan. Our approach detects the presence of meniscal tears in three anatomical sub-regions (anterior horn, meniscal body, posterior horn) for both the Medial Meniscus (MM) and the Lateral Meniscus (LM) individually. For optimal performance of our method, we investigate how to preprocess the MRI data or how to train the CNN such that only relevant information within a Region of Interest (RoI) of the data volume is taken into account for meniscal tear detection. We propose meniscal tear detection combined with a bounding box regressor in a multi-task deep learning framework to let the CNN implicitly consider the corresponding RoIs of the menisci. We evaluate the accuracy of our CNN-based meniscal tear detection approach on 2,399 Double Echo Steady-State (DESS) MRI scans from the Osteoarthritis Initiative database. In addition, to show that our method is capable of generalizing to other MRI sequences, we also adapt our model to Intermediate-Weighted Turbo Spin-Echo (IW TSE) MRI scans. To judge the quality of our approaches, Receiver Operating Characteristic (ROC) curves and Area Under the Curve (AUC) values are evaluated for both MRI sequences. For the detection of tears in DESS MRI, our method reaches AUC values of 0.94, 0.93, 0.93 (anterior horn, body, posterior horn) in MM and 0.96, 0.94, 0.91 in LM. For the detection of tears in IW TSE MRI data, our method yields AUC values of 0.84, 0.88, 0.86 in MM and 0.95, 0.91, 0.90 in LM. In conclusion, the presented method achieves high accuracy for detecting meniscal tears in both DESS and IW TSE MRI data. Furthermore, our method can be easily trained and applied to other MRI sequences. T3 - ZIB-Report - 21-33 Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-84415 SN - 1438-0064 ER - TY - CHAP A1 - Ambellan, Felix A1 - Zachow, Stefan A1 - von Tycowicz, Christoph T1 - Geodesic B-Score for Improved Assessment of Knee Osteoarthritis T2 - Proc. Information Processing in Medical Imaging (IPMI) N2 - Three-dimensional medical imaging enables detailed understanding of osteoarthritis structural status. However, there remains a vast need for automatic, thus, reader-independent measures that provide reliable assessment of subject-specific clinical outcomes. To this end, we derive a consistent generalization of the recently proposed B-score to Riemannian shape spaces. We further present an algorithmic treatment yielding simple, yet efficient computations allowing for analysis of large shape populations with several thousand samples. Our intrinsic formulation exhibits improved discrimination ability over its Euclidean counterpart, which we demonstrate for predictive validity on assessing risks of total knee replacement. This result highlights the potential of the geodesic B-score to enable improved personalized assessment and stratification for interventions. Y1 - 2021 U6 - https://doi.org/10.1007/978-3-030-78191-0_14 SP - 177 EP - 188 ER - TY - JOUR A1 - Hembus, Jessica A1 - Ambellan, Felix A1 - Zachow, Stefan A1 - Bader, Rainer T1 - Establishment of a rolling-sliding test bench to analyze abrasive wear propagation of different bearing materials for knee implants JF - Applied Sciences N2 - Currently, new materials for knee implants need to be extensively and expensive tested in a knee wear simulator in a realized design. However, using a rolling-sliding test bench, these materials can be examined under the same test conditions but with simplified geometries. In the present study, the test bench was optimized, and forces were adapted to the physiological contact pressure in the knee joint using the available geometric parameters. Various polymers made of polyethylene and polyurethane articulating against test wheels made of cobalt-chromium and aluminum titanate were tested in the test bench using adapted forces based on ISO 14243-1. Polyurethane materials showed distinctly higher wear rates than polyethylene materials and showed inadequate wear resistance for use as knee implant material. Thus, the rolling-sliding test bench is an adaptable test setup for evaluating newly developed bearing materials for knee implants. It combines the advantages of screening and simulator tests and allows testing of various bearing materials under physiological load and tribological conditions of the human knee joint. The wear behavior of different material compositions and the influence of surface geometry and quality can be initially investigated without the need to produce complex implant prototypes of total knee endoprosthesis or interpositional spacers. Y1 - 2021 U6 - https://doi.org/10.3390/app11041886 VL - 11 IS - 4 ER - TY - GEN A1 - Tack, Alexander A1 - Ambellan, Felix A1 - Zachow, Stefan T1 - Towards novel osteoarthritis biomarkers: Multi-criteria evaluation of 46,996 segmented knee MRI data from the Osteoarthritis Initiative (Supplementary Material) T2 - PLOS One N2 - Convolutional neural networks (CNNs) are the state-of-the-art for automated assessment of knee osteoarthritis (KOA) from medical image data. However, these methods lack interpretability, mainly focus on image texture, and cannot completely grasp the analyzed anatomies’ shapes. In this study we assess the informative value of quantitative features derived from segmentations in order to assess their potential as an alternative or extension to CNN-based approaches regarding multiple aspects of KOA A fully automated method is employed to segment six anatomical structures around the knee (femoral and tibial bones, femoral and tibial cartilages, and both menisci) in 46,996 MRI scans. Based on these segmentations, quantitative features are computed, i.e., measurements such as cartilage volume, meniscal extrusion and tibial coverage, as well as geometric features based on a statistical shape encoding of the anatomies. The feature quality is assessed by investigating their association to the Kellgren-Lawrence grade (KLG), joint space narrowing (JSN), incident KOA, and total knee replacement (TKR). Using gold standard labels from the Osteoarthritis Initiative database the balanced accuracy (BA), the area under the Receiver Operating Characteristic curve (AUC), and weighted kappa statistics are evaluated. Features based on shape encodings of femur, tibia, and menisci plus the performed measurements showed most potential as KOA biomarkers. Differentiation between healthy and severely arthritic knees yielded BAs of up to 99%, 84% were achieved for diagnosis of early KOA. Substantial agreement with weighted kappa values of 0.73, 0.73, and 0.79 were achieved for classification of the grade of medial JSN, lateral JSN, and KLG, respectively. The AUC was 0.60 and 0.75 for prediction of incident KOA and TKR within 5 years, respectively. Quantitative features from automated segmentations yield excellent results for KLG and JSN classification and show potential for incident KOA and TKR prediction. The validity of these features as KOA biomarkers should be further evaluated, especially as extensions of CNN-based approaches. To foster such developments we make all segmentations publicly available together with this publication. Y1 - 2021 U6 - https://doi.org/10.12752/8328 N1 - 46,996 automated segmentations for data from the OAI database. VL - 16 IS - 10 ER - TY - JOUR A1 - Sekuboyina, Anjany A1 - Husseini, Malek E. A1 - Bayat, Amirhossein A1 - Löffler, Maximilian A1 - Liebl, Hans A1 - Li, Hongwei A1 - Tetteh, Giles A1 - Kukačka, Jan A1 - Payer, Christian A1 - Štern, Darko A1 - Urschler, Martin A1 - Chen, Maodong A1 - Cheng, Dalong A1 - Lessmann, Nikolas A1 - Hu, Yujin A1 - Wang, Tianfu A1 - Yang, Dong A1 - Xu, Daguang A1 - Ambellan, Felix A1 - Amiranashvili, Tamaz A1 - Ehlke, Moritz A1 - Lamecker, Hans A1 - Lehnert, Sebastian A1 - Lirio, Marilia A1 - de Olaguer, Nicolás Pérez A1 - Ramm, Heiko A1 - Sahu, Manish A1 - Tack, Alexander A1 - Zachow, Stefan A1 - Jiang, Tao A1 - Ma, Xinjun A1 - Angerman, Christoph A1 - Wang, Xin A1 - Brown, Kevin A1 - Kirszenberg, Alexandre A1 - Puybareau, Élodie A1 - Chen, Di A1 - Bai, Yiwei A1 - Rapazzo, Brandon H. A1 - Yeah, Timyoas A1 - Zhang, Amber A1 - Xu, Shangliang A1 - Hou, Feng A1 - He, Zhiqiang A1 - Zeng, Chan A1 - Xiangshang, Zheng A1 - Liming, Xu A1 - Netherton, Tucker J. A1 - Mumme, Raymond P. A1 - Court, Laurence E. A1 - Huang, Zixun A1 - He, Chenhang A1 - Wang, Li-Wen A1 - Ling, Sai Ho A1 - Huynh, Lê Duy A1 - Boutry, Nicolas A1 - Jakubicek, Roman A1 - Chmelik, Jiri A1 - Mulay, Supriti A1 - Sivaprakasam, Mohanasankar A1 - Paetzold, Johannes C. A1 - Shit, Suprosanna A1 - Ezhov, Ivan A1 - Wiestler, Benedikt A1 - Glocker, Ben A1 - Valentinitsch, Alexander A1 - Rempfler, Markus A1 - Menze, Björn H. A1 - Kirschke, Jan S. T1 - VerSe: A Vertebrae labelling and segmentation benchmark for multi-detector CT images JF - Medical Image Analysis N2 - Vertebral labelling and segmentation are two fundamental tasks in an automated spine processing pipeline. Reliable and accurate processing of spine images is expected to benefit clinical decision support systems for diagnosis, surgery planning, and population-based analysis of spine and bone health. However, designing automated algorithms for spine processing is challenging predominantly due to considerable variations in anatomy and acquisition protocols and due to a severe shortage of publicly available data. Addressing these limitations, the Large Scale Vertebrae Segmentation Challenge (VerSe) was organised in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2019 and 2020, with a call for algorithms tackling the labelling and segmentation of vertebrae. Two datasets containing a total of 374 multi-detector CT scans from 355 patients were prepared and 4505 vertebrae have individually been annotated at voxel level by a human-machine hybrid algorithm (https://osf.io/nqjyw/, https://osf.io/t98fz/). A total of 25 algorithms were benchmarked on these datasets. In this work, we present the results of this evaluation and further investigate the performance variation at the vertebra level, scan level, and different fields of view. We also evaluate the generalisability of the approaches to an implicit domain shift in data by evaluating the top-performing algorithms of one challenge iteration on data from the other iteration. The principal takeaway from VerSe: the performance of an algorithm in labelling and segmenting a spine scan hinges on its ability to correctly identify vertebrae in cases of rare anatomical variations. The VerSe content and code can be accessed at: https://github.com/anjany/verse. Y1 - 2021 U6 - https://doi.org/10.1016/j.media.2021.102166 VL - 73 ER - TY - JOUR A1 - Picht, Thomas A1 - Le Calve, Maxime A1 - Tomasello, Rosario A1 - Fekonja, Lucius A1 - Gholami, Mohammad Fardin A1 - Bruhn, Matthias A1 - Zwick, Carola A1 - Rabe, Jürgen P. A1 - Müller-Birn, Claudia A1 - Vajkoczy, Peter A1 - Sauer, Igor M. A1 - Zachow, Stefan A1 - Nyakatura, John A. A1 - Ribault, Patricia A1 - Pulvermüller, Friedemann T1 - A note on neurosurgical resection and why we need to rethink cutting JF - Neurosurgery Y1 - 2021 U6 - https://doi.org/10.1093/neuros/nyab326 VL - 89 IS - 5 SP - 289 EP - 291 ER - TY - GEN A1 - Ambellan, Felix A1 - Zachow, Stefan A1 - von Tycowicz, Christoph T1 - Geodesic B-Score for Improved Assessment of Knee Osteoarthritis N2 - Three-dimensional medical imaging enables detailed understanding of osteoarthritis structural status. However, there remains a vast need for automatic, thus, reader-independent measures that provide reliable assessment of subject-specific clinical outcomes. To this end, we derive a consistent generalization of the recently proposed B-score to Riemannian shape spaces. We further present an algorithmic treatment yielding simple, yet efficient computations allowing for analysis of large shape populations with several thousand samples. Our intrinsic formulation exhibits improved discrimination ability over its Euclidean counterpart, which we demonstrate for predictive validity on assessing risks of total knee replacement. This result highlights the potential of the geodesic B-score to enable improved personalized assessment and stratification for interventions. T3 - ZIB-Report - 21-09 KW - Statistical shape analysis KW - Osteoarthritis KW - Geometric statistics KW - Riemannian manifolds Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-81930 SN - 1438-0064 ER - TY - JOUR A1 - Grewe, Carl Martin A1 - Liu, Tuo A1 - Kahl, Christoph A1 - Andrea, Hildebrandt A1 - Zachow, Stefan T1 - Statistical Learning of Facial Expressions Improves Realism of Animated Avatar Faces JF - Frontiers in Virtual Reality Y1 - 2021 U6 - https://doi.org/10.3389/frvir.2021.619811 VL - 2 SP - 1 EP - 13 PB - Frontiers ER - TY - JOUR A1 - Brüning, Jan A1 - Hildebrandt, Thomas A1 - Heppt, Werner A1 - Schmidt, Nora A1 - Lamecker, Hans A1 - Szengel, Angelika A1 - Amiridze, Natalja A1 - Ramm, Heiko A1 - Bindernagel, Matthias A1 - Zachow, Stefan A1 - Goubergrits, Leonid T1 - Characterization of the Airflow within an Average Geometry of the Healthy Human Nasal Cavity JF - Scientific Reports N2 - This study’s objective was the generation of a standardized geometry of the healthy nasal cavity. An average geometry of the healthy nasal cavity was generated using a statistical shape model based on 25 symptom-free subjects. Airflow within the average geometry and these geometries was calculated using fluid simulations. Integral measures of the nasal resistance, wall shear stresses (WSS) and velocities were calculated as well as cross-sectional areas (CSA). Furthermore, individual WSS and static pressure distributions were mapped onto the average geometry. The average geometry featured an overall more regular shape that resulted in less resistance, reduced wall shear stresses and velocities compared to the median of the 25 geometries. Spatial distributions of WSS and pressure of average geometry agreed well compared to the average distributions of all individual geometries. The minimal CSA of the average geometry was larger than the median of all individual geometries (83.4 vs. 74.7 mm²). The airflow observed within the average geometry of the healthy nasal cavity did not equal the average airflow of the individual geometries. While differences observed for integral measures were notable, the calculated values for the average geometry lay within the distributions of the individual parameters. Spatially resolved parameters differed less prominently. Y1 - 2020 UR - https://rdcu.be/b2irD U6 - https://doi.org/10.1038/s41598-020-60755-3 VL - 3755 IS - 10 ER - TY - CHAP A1 - Sahu, Manish A1 - Strömsdörfer, Ronja A1 - Mukhopadhyay, Anirban A1 - Zachow, Stefan T1 - Endo-Sim2Real: Consistency learning-based domain adaptation for instrument segmentation T2 - Proc. Medical Image Computing and Computer Assisted Intervention (MICCAI), Part III N2 - Surgical tool segmentation in endoscopic videos is an important component of computer assisted interventions systems. Recent success of image-based solutions using fully-supervised deep learning approaches can be attributed to the collection of big labeled datasets. However, the annotation of a big dataset of real videos can be prohibitively expensive and time consuming. Computer simulations could alleviate the manual labeling problem, however, models trained on simulated data do not generalize to real data. This work proposes a consistency-based framework for joint learning of simulated and real (unlabeled) endoscopic data to bridge this performance generalization issue. Empirical results on two data sets (15 videos of the Cholec80 and EndoVis'15 dataset) highlight the effectiveness of the proposed Endo-Sim2Real method for instrument segmentation. We compare the segmentation of the proposed approach with state-of-the-art solutions and show that our method improves segmentation both in terms of quality and quantity. Y1 - 2020 U6 - https://doi.org/https://doi.org/10.1007/978-3-030-59716-0_75 VL - 12263 PB - Springer Nature ER - TY - JOUR A1 - Sahu, Manish A1 - Szengel, Angelika A1 - Mukhopadhyay, Anirban A1 - Zachow, Stefan T1 - Surgical phase recognition by learning phase transitions JF - Current Directions in Biomedical Engineering (CDBME) N2 - Automatic recognition of surgical phases is an important component for developing an intra-operative context-aware system. Prior work in this area focuses on recognizing short-term tool usage patterns within surgical phases. However, the difference between intra- and inter-phase tool usage patterns has not been investigated for automatic phase recognition. We developed a Recurrent Neural Network (RNN), in particular a state-preserving Long Short Term Memory (LSTM) architecture to utilize the long-term evolution of tool usage within complete surgical procedures. For fully automatic tool presence detection from surgical video frames, a Convolutional Neural Network (CNN) based architecture namely ZIBNet is employed. Our proposed approach outperformed EndoNet by 8.1% on overall precision for phase detection tasks and 12.5% on meanAP for tool recognition tasks. Y1 - 2020 U6 - https://doi.org/https://doi.org/10.1515/cdbme-2020-0037 N1 - Nomination for the Best-Paper Award VL - 6 IS - 1 SP - 20200037 PB - De Gruyter ER - TY - GEN A1 - Sahu, Manish A1 - Szengel, Angelika A1 - Mukhopadhyay, Anirban A1 - Zachow, Stefan T1 - Analyzing laparoscopic cholecystectomy with deep learning: automatic detection of surgical tools and phases T2 - 28th International Congress of the European Association for Endoscopic Surgery (EAES) N2 - Motivation: The ever-rising volume of patients, high maintenance cost of operating rooms and time consuming analysis of surgical skills are fundamental problems that hamper the practical training of the next generation of surgeons. The hospitals prefer to keep the surgeons busy in real operations over training young surgeons for obvious economic reasons. One fundamental need in surgical training is the reduction of the time needed by the senior surgeon to review the endoscopic procedures performed by the young surgeon while minimizing the subjective bias in evaluation. The unprecedented performance of deep learning ushers the new age of data-driven automatic analysis of surgical skills. Method: Deep learning is capable of efficiently analyzing thousands of hours of laparoscopic video footage to provide an objective assessment of surgical skills. However, the traditional end-to-end setting of deep learning (video in, skill assessment out) is not explainable. Our strategy is to utilize the surgical process modeling framework to divide the surgical process into understandable components. This provides the opportunity to employ deep learning for superior yet automatic detection and evaluation of several aspects of laparoscopic cholecystectomy such as surgical tool and phase detection. We employ ZIBNet for the detection of surgical tool presence. ZIBNet employs pre-processing based on tool usage imbalance, a transfer learned 50-layer residual network (ResNet-50) and temporal smoothing. To encode the temporal evolution of tool usage (over the entire video sequence) that relates to the surgical phases, Long Short Term Memory (LSTM) units are employed with long-term dependency. Dataset: We used CHOLEC 80 dataset that consists of 80 videos of laparoscopic cholecystectomy performed by 13 surgeons, divided equally for training and testing. In these videos, up to three different tools (among 7 types of tools) can be present in a frame. Results: The mean average precision of the detection of all tools is 93.5 ranging between 86.8 and 99.3, a significant improvement (p <0.01) over the previous state-of-the-art. We observed that less frequent tools like Scissors, Irrigator, Specimen Bag etc. are more related to phase transitions. The overall precision (recall) of the detection of all surgical phases is 79.6 (81.3). Conclusion: While this is not the end goal for surgical skill analysis, the development of such a technological platform is essential toward a data-driven objective understanding of surgical skills. In future, we plan to investigate surgeon-in-the-loop analysis and feedback for surgical skill analysis. Y1 - 2020 UR - https://academy.eaes.eu/eaes/2020/28th/298882/manish.sahu.analyzing.laparoscopic.cholecystectomy.with.deep.learning.html?f=listing%3D0%2Abrowseby%3D8%2Asortby%3D2 ER - TY - JOUR A1 - Pimentel, Pedro A1 - Szengel, Angelika A1 - Ehlke, Moritz A1 - Lamecker, Hans A1 - Zachow, Stefan A1 - Estacio, Laura A1 - Doenitz, Christian A1 - Ramm, Heiko ED - Li, Jianning ED - Egger, Jan T1 - Automated Virtual Reconstruction of Large Skull Defects using Statistical Shape Models and Generative Adversarial Networks BT - First Challenge, AutoImplant 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, Proceedings JF - Towards the Automatization of Cranial Implant Design in Cranioplasty N2 - We present an automated method for extrapolating missing regions in label data of the skull in an anatomically plausible manner. The ultimate goal is to design patient-speci� c cranial implants for correcting large, arbitrarily shaped defects of the skull that can, for example, result from trauma of the head. Our approach utilizes a 3D statistical shape model (SSM) of the skull and a 2D generative adversarial network (GAN) that is trained in an unsupervised fashion from samples of healthy patients alone. By � tting the SSM to given input labels containing the skull defect, a First approximation of the healthy state of the patient is obtained. The GAN is then applied to further correct and smooth the output of the SSM in an anatomically plausible manner. Finally, the defect region is extracted using morphological operations and subtraction between the extrapolated healthy state of the patient and the defective input labels. The method is trained and evaluated based on data from the MICCAI 2020 AutoImplant challenge. It produces state-of-the art results on regularly shaped cut-outs that were present in the training and testing data of the challenge. Furthermore, due to unsupervised nature of the approach, the method generalizes well to previously unseen defects of varying shapes that were only present in the hidden test dataset. Y1 - 2020 U6 - https://doi.org/10.1007/978-3-030-64327-0_3 N1 - Best Paper Award VL - 12439 SP - 16 EP - 27 PB - Springer International Publishing ET - 1 ER - TY - JOUR A1 - Sekuboyina, Anjany A1 - Bayat, Amirhossein A1 - Husseini, Malek E. A1 - Löffler, Maximilian A1 - Li, Hongwei A1 - Tetteh, Giles A1 - Kukačka, Jan A1 - Payer, Christian A1 - Štern, Darko A1 - Urschler, Martin A1 - Chen, Maodong A1 - Cheng, Dalong A1 - Lessmann, Nikolas A1 - Hu, Yujin A1 - Wang, Tianfu A1 - Yang, Dong A1 - Xu, Daguang A1 - Ambellan, Felix A1 - Amiranashvili, Tamaz A1 - Ehlke, Moritz A1 - Lamecker, Hans A1 - Lehnert, Sebastian A1 - Lirio, Marilia A1 - de Olaguer, Nicolás Pérez A1 - Ramm, Heiko A1 - Sahu, Manish A1 - Tack, Alexander A1 - Zachow, Stefan A1 - Jiang, Tao A1 - Ma, Xinjun A1 - Angerman, Christoph A1 - Wang, Xin A1 - Wei, Qingyue A1 - Brown, Kevin A1 - Wolf, Matthias A1 - Kirszenberg, Alexandre A1 - Puybareau, Élodie A1 - Valentinitsch, Alexander A1 - Rempfler, Markus A1 - Menze, Björn H. A1 - Kirschke, Jan S. T1 - VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images JF - arXiv Y1 - 2020 ER - TY - GEN A1 - Ambellan, Felix A1 - Lamecker, Hans A1 - von Tycowicz, Christoph A1 - Zachow, Stefan T1 - Statistical Shape Models - Understanding and Mastering Variation in Anatomy N2 - In our chapter we are describing how to reconstruct three-dimensional anatomy from medical image data and how to build Statistical 3D Shape Models out of many such reconstructions yielding a new kind of anatomy that not only allows quantitative analysis of anatomical variation but also a visual exploration and educational visualization. Future digital anatomy atlases will not only show a static (average) anatomy but also its normal or pathological variation in three or even four dimensions, hence, illustrating growth and/or disease progression. Statistical Shape Models (SSMs) are geometric models that describe a collection of semantically similar objects in a very compact way. SSMs represent an average shape of many three-dimensional objects as well as their variation in shape. The creation of SSMs requires a correspondence mapping, which can be achieved e.g. by parameterization with a respective sampling. If a corresponding parameterization over all shapes can be established, variation between individual shape characteristics can be mathematically investigated. We will explain what Statistical Shape Models are and how they are constructed. Extensions of Statistical Shape Models will be motivated for articulated coupled structures. In addition to shape also the appearance of objects will be integrated into the concept. Appearance is a visual feature independent of shape that depends on observers or imaging techniques. Typical appearances are for instance the color and intensity of a visual surface of an object under particular lighting conditions, or measurements of material properties with computed tomography (CT) or magnetic resonance imaging (MRI). A combination of (articulated) statistical shape models with statistical models of appearance lead to articulated Statistical Shape and Appearance Models (a-SSAMs).After giving various examples of SSMs for human organs, skeletal structures, faces, and bodies, we will shortly describe clinical applications where such models have been successfully employed. Statistical Shape Models are the foundation for the analysis of anatomical cohort data, where characteristic shapes are correlated to demographic or epidemiologic data. SSMs consisting of several thousands of objects offer, in combination with statistical methods ormachine learning techniques, the possibility to identify characteristic clusters, thus being the foundation for advanced diagnostic disease scoring. T3 - ZIB-Report - 19-13 Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-72699 SN - 1438-0064 ER - TY - GEN A1 - Ambellan, Felix A1 - Tack, Alexander A1 - Ehlke, Moritz A1 - Zachow, Stefan T1 - Automated Segmentation of Knee Bone and Cartilage combining Statistical Shape Knowledge and Convolutional Neural Networks: Data from the Osteoarthritis Initiative N2 - We present a method for the automated segmentation of knee bones and cartilage from magnetic resonance imaging (MRI) that combines a priori knowledge of anatomical shape with Convolutional Neural Networks (CNNs).The proposed approach incorporates 3D Statistical Shape Models (SSMs) as well as 2D and 3D CNNs to achieve a robust and accurate segmentation of even highly pathological knee structures.The shape models and neural networks employed are trained using data from the Osteoarthritis Initiative (OAI) and the MICCAI grand challenge "Segmentation of Knee Images 2010" (SKI10), respectively. We evaluate our method on 40 validation and 50 submission datasets from the SKI10 challenge.For the first time, an accuracy equivalent to the inter-observer variability of human readers is achieved in this challenge.Moreover, the quality of the proposed method is thoroughly assessed using various measures for data from the OAI, i.e. 507 manual segmentations of bone and cartilage, and 88 additional manual segmentations of cartilage. Our method yields sub-voxel accuracy for both OAI datasets. We make the 507 manual segmentations as well as our experimental setup publicly available to further aid research in the field of medical image segmentation.In conclusion, combining localized classification via CNNs with statistical anatomical knowledge via SSMs results in a state-of-the-art segmentation method for knee bones and cartilage from MRI data. T3 - ZIB-Report - 19-06 Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-72704 SN - 1438-0064 N1 - Innovation Excellence Award 2020 ER - TY - CHAP A1 - Ambellan, Felix A1 - Lamecker, Hans A1 - von Tycowicz, Christoph A1 - Zachow, Stefan ED - Rea, Paul M. T1 - Statistical Shape Models - Understanding and Mastering Variation in Anatomy T2 - Biomedical Visualisation N2 - In our chapter we are describing how to reconstruct three-dimensional anatomy from medical image data and how to build Statistical 3D Shape Models out of many such reconstructions yielding a new kind of anatomy that not only allows quantitative analysis of anatomical variation but also a visual exploration and educational visualization. Future digital anatomy atlases will not only show a static (average) anatomy but also its normal or pathological variation in three or even four dimensions, hence, illustrating growth and/or disease progression. Statistical Shape Models (SSMs) are geometric models that describe a collection of semantically similar objects in a very compact way. SSMs represent an average shape of many three-dimensional objects as well as their variation in shape. The creation of SSMs requires a correspondence mapping, which can be achieved e.g. by parameterization with a respective sampling. If a corresponding parameterization over all shapes can be established, variation between individual shape characteristics can be mathematically investigated. We will explain what Statistical Shape Models are and how they are constructed. Extensions of Statistical Shape Models will be motivated for articulated coupled structures. In addition to shape also the appearance of objects will be integrated into the concept. Appearance is a visual feature independent of shape that depends on observers or imaging techniques. Typical appearances are for instance the color and intensity of a visual surface of an object under particular lighting conditions, or measurements of material properties with computed tomography (CT) or magnetic resonance imaging (MRI). A combination of (articulated) statistical shape models with statistical models of appearance lead to articulated Statistical Shape and Appearance Models (a-SSAMs).After giving various examples of SSMs for human organs, skeletal structures, faces, and bodies, we will shortly describe clinical applications where such models have been successfully employed. Statistical Shape Models are the foundation for the analysis of anatomical cohort data, where characteristic shapes are correlated to demographic or epidemiologic data. SSMs consisting of several thousands of objects offer, in combination with statistical methods ormachine learning techniques, the possibility to identify characteristic clusters, thus being the foundation for advanced diagnostic disease scoring. Y1 - 2019 SN - 978-3-030-19384-3 SN - 978-3-030-19385-0 U6 - https://doi.org/10.1007/978-3-030-19385-0_5 VL - 3 IS - 1156 SP - 67 EP - 84 PB - Springer Nature Switzerland AG ET - 1 ER - TY - JOUR A1 - Hildebrandt, Thomas A1 - Bruening, Jan Joris A1 - Schmidt, Nora Laura A1 - Lamecker, Hans A1 - Heppt, Werner A1 - Zachow, Stefan A1 - Goubergrits, Leonid T1 - The Healthy Nasal Cavity - Characteristics of Morphology and Related Airflow Based on a Statistical Shape Model Viewed from a Surgeon’s Perspective JF - Facial Plastic Surgery N2 - Functional surgery on the nasal framework requires referential criteria to objectively assess nasal breathing for indication and follow-up. Thismotivated us to generate amean geometry of the nasal cavity based on a statistical shape model. In this study, the authors could demonstrate that the introduced nasal cavity’s mean geometry features characteristics of the inner shape and airflow, which are commonly observed in symptom-free subjects. Therefore, the mean geometry might serve as a reference-like model when one considers qualitative aspects. However, to facilitate quantitative considerations and statistical inference, further research is necessary. Additionally, the authorswere able to obtain details about the importance of the isthmus nasi and the inferior turbinate for the intranasal airstream. KW - statistical shape model KW - nasal cavity KW - nasal breathing KW - nasal airflow KW - isthmus nasi KW - inferior turbinate Y1 - 2019 U6 - https://doi.org/10.1055/s-0039-1677721 VL - 35 IS - 1 SP - 9 EP - 13 ER - TY - JOUR A1 - Hildebrandt, Thomas A1 - Bruening, Jan Joris A1 - Lamecker, Hans A1 - Zachow, Stefan A1 - Heppt, Werner A1 - Schmidt, Nora A1 - Goubergrits, Leonid T1 - Digital Analysis of Nasal Airflow Facilitating Decision Support in Rhinosurgery JF - Facial Plastic Surgery N2 - Successful functional surgery on the nasal framework requires reliable and comprehensive diagnosis. In this regard, the authors introduce a new methodology: Digital Analysis of Nasal Airflow (diANA). It is based on computational fluid dynamics, a statistical shape model of the healthy nasal cavity and rhinologic expertise. diANA necessitates an anonymized tomographic dataset of the paranasal sinuses including the complete nasal cavity and, when available, clinical information. The principle of diANA is to compare the morphology and the respective airflow of an individual nose with those of a reference. This enablesmorphometric aberrations and consecutive flow field anomalies to localize and quantify within a patient’s nasal cavity. Finally, an elaborated expert opinion with instructive visualizations is provided. Using diANA might support surgeons in decision-making, avoiding unnecessary surgery, gaining more precision, and target-orientation for indicated operations. KW - nasal airflow simulation KW - nasal breathing KW - statistical shape model KW - diANA KW - nasal obstruction KW - rhinorespiratory homeostasis Y1 - 2019 U6 - https://doi.org/10.1055/s-0039-1677720 VL - 35 IS - 1 SP - 1 EP - 8 ER -