@inproceedings{JoachimskyAmbellanZachow, author = {Joachimsky, Robert and Ambellan, Felix and Zachow, Stefan}, title = {Computerassistierte Auswahl und Platzierung von interpositionalen Spacern zur Behandlung fr{\"u}her Gonarthrose}, series = {Proceedings of the Jahrestagung der Deutschen Gesellschaft f{\"u}r Computer- und Roboterassistierte Chirurgie (CURAC)}, volume = {16}, booktitle = {Proceedings of the Jahrestagung der Deutschen Gesellschaft f{\"u}r Computer- und Roboterassistierte Chirurgie (CURAC)}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-65321}, pages = {106 -- 111}, abstract = {Degenerative Gelenkerkrankungen, wie die Osteoarthrose, sind ein h{\"a}ufiges Krankheitsbild unter {\"a}lteren Erwachsenen. Hierbei verringert sich u.a. der Gelenkspalt aufgrund degenerierten Knorpels oder gesch{\"a}digter Menisci. Ein in den Gelenkspalt eingebrachter interpositionaler Spacer soll die mit der Osteoarthrose einhergehende verringerte Gelenkkontaktfl{\"a}che erh{\"o}hen und so der teilweise oder vollst{\"a}ndige Gelenkersatz hinausgez{\"o}gert oder vermieden werden. In dieser Arbeit pr{\"a}sentieren wir eine Planungssoftware f{\"u}r die Auswahl und Positionierung eines interpositionalen Spacers am Patientenmodell. Auf einer MRT-basierten Bildsegmentierung aufbauend erfolgt eine geometrische Rekonstruktion der 3D-Anatomie des Kniegelenks. Anhand dieser wird der Gelenkspalt bestimmt, sowie ein Spacer ausgew{\"a}hlt und algorithmisch vorpositioniert. Die Positionierung des Spacers ist durch den Benutzer jederzeit interaktiv anpassbar. F{\"u}r jede Positionierung eines Spacers wird ein Fitness-Wert zur Knieanatomie des jeweiligen Patienten berechnet und den Nutzern R{\"u}ckmeldung hinsichtlich Passgenauigkeit gegeben. Die Software unterst{\"u}tzt somit als Entscheidungshilfe die behandelnden {\"A}rzte bei der patientenspezifischen Spacerauswahl.}, language = {de} } @inproceedings{AmbellanTackWilsonetal., author = {Ambellan, Felix and Tack, Alexander and Wilson, Dave and Anglin, Carolyn and Lamecker, Hans and Zachow, Stefan}, title = {Evaluating two methods for Geometry Reconstruction from Sparse Surgical Navigation Data}, series = {Proceedings of the Jahrestagung der Deutschen Gesellschaft f{\"u}r Computer- und Roboterassistierte Chirurgie (CURAC)}, volume = {16}, booktitle = {Proceedings of the Jahrestagung der Deutschen Gesellschaft f{\"u}r Computer- und Roboterassistierte Chirurgie (CURAC)}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-65339}, pages = {24 -- 30}, abstract = {In this study we investigate methods for fitting a Statistical Shape Model (SSM) to intraoperatively acquired point cloud data from a surgical navigation system. We validate the fitted models against the pre-operatively acquired Magnetic Resonance Imaging (MRI) data from the same patients. We consider a cohort of 10 patients who underwent navigated total knee arthroplasty. As part of the surgical protocol the patients' distal femurs were partially digitized. All patients had an MRI scan two months pre-operatively. The MRI data were manually segmented and the reconstructed bone surfaces used as ground truth against which the fit was compared. Two methods were used to fit the SSM to the data, based on (1) Iterative Closest Points (ICP) and (2) Gaussian Mixture Models (GMM). For both approaches, the difference between model fit and ground truth surface averaged less than 1.7 mm and excellent correspondence with the distal femoral morphology can be demonstrated.}, language = {en} } @inproceedings{AmbellanZachowvonTycowicz, author = {Ambellan, Felix and Zachow, Stefan and von Tycowicz, Christoph}, title = {An as-invariant-as-possible GL+(3)-based Statistical Shape Model}, series = {Proc. 7th MICCAI workshop on Mathematical Foundations of Computational Anatomy (MFCA)}, volume = {11846}, booktitle = {Proc. 7th MICCAI workshop on Mathematical Foundations of Computational Anatomy (MFCA)}, publisher = {Springer}, doi = {10.1007/978-3-030-33226-6_23}, pages = {219 -- 228}, abstract = {We describe a novel nonlinear statistical shape model basedon differential coordinates viewed as elements of GL+(3). We adopt an as-invariant-as possible framework comprising a bi-invariant Lie group mean and a tangent principal component analysis based on a unique GL+(3)-left-invariant, O(3)-right-invariant metric. Contrary to earlier work that equips the coordinates with a specifically constructed group structure, our method employs the inherent geometric structure of the group-valued data and therefore features an improved statistical power in identifying shape differences. We demonstrate this in experiments on two anatomical datasets including comparison to the standard Euclidean as well as recent state-of-the-art nonlinear approaches to statistical shape modeling.}, language = {en} } @inproceedings{AmbellanZachowvonTycowicz, author = {Ambellan, Felix and Zachow, Stefan and von Tycowicz, Christoph}, title = {Geodesic B-Score for Improved Assessment of Knee Osteoarthritis}, series = {Proc. Information Processing in Medical Imaging (IPMI)}, booktitle = {Proc. Information Processing in Medical Imaging (IPMI)}, doi = {10.1007/978-3-030-78191-0_14}, pages = {177 -- 188}, abstract = {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.}, language = {en} } @inproceedings{AmbellanZachowvonTycowicz, author = {Ambellan, Felix and Zachow, Stefan and von Tycowicz, Christoph}, title = {A Surface-Theoretic Approach for Statistical Shape Modeling}, series = {Proc. Medical Image Computing and Computer Assisted Intervention (MICCAI), Part IV}, volume = {11767}, booktitle = {Proc. Medical Image Computing and Computer Assisted Intervention (MICCAI), Part IV}, publisher = {Springer}, doi = {10.1007/978-3-030-32251-9_3}, pages = {21 -- 29}, abstract = {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. We evaluate the performance of our model w.r.t. shape-based classification of pathological malformations of the human knee and show that it outperforms 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 natural biological shape variability, we carry out an analysis of specificity and generalization ability.}, language = {en} } @inproceedings{AmbellanTackEhlkeetal., author = {Ambellan, Felix and Tack, Alexander and Ehlke, Moritz and Zachow, Stefan}, title = {Automated Segmentation of Knee Bone and Cartilage combining Statistical Shape Knowledge and Convolutional Neural Networks: Data from the Osteoarthritis Initiative}, series = {Medical Imaging with Deep Learning}, booktitle = {Medical Imaging with Deep Learning}, abstract = {We present a method for the automated segmentation of knee bones and cartilage from magnetic resonance imaging, 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 method is evaluated on data of the MICCAI grand challenge "Segmentation of Knee Images 2010". For the first time an accuracy equivalent to the inter-observer variability of human readers has been achieved in this challenge. Moreover, the quality of the proposed method is thoroughly assessed using various measures for 507 manual segmentations of bone and cartilage, and 88 additional manual segmentations of cartilage. Our method yields sub-voxel accuracy. In conclusion, combining of anatomical knowledge using SSMs with localized classification via CNNs results in a state-of-the-art segmentation method.}, language = {en} } @inproceedings{MyersUtpalaTalbaretal., author = {Myers, Adele and Utpala, Saiteja and Talbar, Shubham and Sanborn, Sophia and Shewmake, Christian and Donnat, Claire and Mathe, Johan and Lupo, Umberto and Sonthalia, Rishi and Cui, Xinyue and Szwagier, Tom and Pignet, Arthur and Bergsson, Andri and Hauberg, S{\o}ren and Nielsen, Dmitriy and Sommer, Stefan and Klindt, David and Hermansen, Erik and Vaupel, Melvin and Dunn, Benjamin and Xiong, Jeffrey and Aharony, Noga and Pe'er, Itsik and Ambellan, Felix and Hanik, Martin and Navayazdani, Esfandiar and Tycowicz, Christoph von and Miolane, Nina}, title = {ICLR 2022 Challenge for Computational Geomerty \& Topology: Design and Results}, series = {Proceedings of Topology, Algebra, and Geometry in Learning}, volume = {196}, booktitle = {Proceedings of Topology, Algebra, and Geometry in Learning}, publisher = {PMLR}, pages = {269 -- 276}, language = {en} } @inproceedings{LuedkeAmiranashviliAmbellanetal., author = {L{\"u}dke, David and Amiranashvili, Tamaz and Ambellan, Felix and Ezhov, Ivan and Menze, Bjoern and Zachow, Stefan}, title = {Landmark-free Statistical Shape Modeling via Neural Flow Deformations}, series = {Medical Image Computing and Computer Assisted Intervention - MICCAI 2022}, volume = {13432}, booktitle = {Medical Image Computing and Computer Assisted Intervention - MICCAI 2022}, publisher = {Springer, Cham}, doi = {10.1007/978-3-031-16434-7_44}, abstract = {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).}, language = {en} } @inproceedings{MayerBaumAmbellanetal., author = {Mayer, Julius and Baum, Daniel and Ambellan, Felix and von Tycowicz, Christoph}, title = {A Soft-Correspondence Approach to Shape-based Disease Grading with Graph Convolutional Networks}, series = {Proceedings of Machine Learning Research}, volume = {194}, booktitle = {Proceedings of Machine Learning Research}, pages = {85 -- 95}, abstract = {Shape analysis provides principled means for understanding anatomical structures from medical images. The underlying notions of shape spaces, however, come with strict assumptions prohibiting the analysis of incomplete and/or topologically varying shapes. This work aims to alleviate these limitations by adapting the concept of soft correspondences. In particular, we present a graph-based learning approach for morphometric classification of disease states that is based on a generalized notion of shape correspondences in terms of functional maps. We demonstrate the performance of the derived classifier on the open-access ADNI database for differentiating normal controls and subjects with Alzheimer's disease. Notably, our experiment shows that our approach can improve over state-of-the-art from geometric deep learning.}, language = {en} }