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 - 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 - 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 - 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 -