Morphological Scoring of Disease States: Learning from Large Anatomical Databases
The understanding of anatomical shapes as well as their variations over time or among individuals is crucial in medical diagnostics and therapy. Riemannian shape statistics in combination with deep learning may lead to novel, data-driven approaches for assessment of anomalies and morphological scoring in medicine.
This project is part of the Berlin Mathematics Research Center MATH+, a detailed project website can be found here at mathplus.de.
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Georg Duda (co-PI @ Julius Wolff Institute - Charité - Universitätsmedizin Berlin
1000shapes GmbH - Berlin
Funding:
Berlin Mathematics Research Center MATH+
Publications
2022
2021
2020
2019
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| Felix Ambellan | Efficient Riemannian Statistical Shape Analysis with Applications in Disease Assessment | Doctoral thesis, Freie Universität Berlin, Christof Schütte, Christoph von Tycowicz (Advisors), 2022 |
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| David Lüdke, Tamaz Amiranashvili, Felix Ambellan, Ivan Ezhov, Bjoern Menze, Stefan Zachow | Landmark-free Statistical Shape Modeling via Neural Flow Deformations | Medical Image Computing and Computer Assisted Intervention - MICCAI 2022, Vol.13432, Lecture Notes in Computer Science, 2022 |
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2021 |
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| Felix Ambellan, Stefan Zachow, Christoph von Tycowicz | Geodesic B-Score for Improved Assessment of Knee Osteoarthritis | Proc. Information Processing in Medical Imaging (IPMI), pp. 177-188, Lecture Notes in Computer Science, 2021 (preprint available as ZIB-Report 21-09) |
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| Felix Ambellan, Martin Hanik, Christoph von Tycowicz | Morphomatics: Geometric morphometrics in non-Euclidean shape spaces | 2021 |
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| Felix Ambellan, Stefan Zachow, Christoph von Tycowicz | Rigid Motion Invariant Statistical Shape Modeling based on Discrete Fundamental Forms | Medical Image Analysis, Vol.73, 2021 |
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DOI arXiv |
| Anjany Sekuboyina, Malek E. Husseini, Amirhossein Bayat, Maximilian Löffler, Hans Liebl, Hongwei Li, Giles Tetteh, Jan Kukačka, Christian Payer, Darko Štern, Martin Urschler, Maodong Chen, Dalong Cheng, Nikolas Lessmann, Yujin Hu, Tianfu Wang, Dong Yang, Daguang Xu, Felix Ambellan, Tamaz Amiranashvili, Moritz Ehlke, Hans Lamecker, Sebastian Lehnert, Marilia Lirio, Nicolás Pérez de Olaguer, Heiko Ramm, Manish Sahu, Alexander Tack, Stefan Zachow, Tao Jiang, Xinjun Ma, Christoph Angerman, Xin Wang, Kevin Brown, Alexandre Kirszenberg, Élodie Puybareau, Di Chen, Yiwei Bai, Brandon H. Rapazzo, Timyoas Yeah, Amber Zhang, Shangliang Xu, Feng Hou, Zhiqiang He, Chan Zeng, Zheng Xiangshang, Xu Liming, Tucker J. Netherton, Raymond P. Mumme, Laurence E. Court, Zixun Huang, Chenhang He, Li-Wen Wang, Sai Ho Ling, Lê Duy Huynh, Nicolas Boutry, Roman Jakubicek, Jiri Chmelik, Supriti Mulay, Mohanasankar Sivaprakasam, Johannes C. Paetzold, Suprosanna Shit, Ivan Ezhov, Benedikt Wiestler, Ben Glocker, Alexander Valentinitsch, Markus Rempfler, Björn H. Menze, Jan S. Kirschke | VerSe: A Vertebrae labelling and segmentation benchmark for multi-detector CT images | Medical Image Analysis, Vol.73, 2021 |
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2020 |
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| Anjany Sekuboyina, Amirhossein Bayat, Malek E. Husseini, Maximilian Löffler, Hongwei Li, Giles Tetteh, Jan Kukačka, Christian Payer, Darko Štern, Martin Urschler, Maodong Chen, Dalong Cheng, Nikolas Lessmann, Yujin Hu, Tianfu Wang, Dong Yang, Daguang Xu, Felix Ambellan, Tamaz Amiranashvili, Moritz Ehlke, Hans Lamecker, Sebastian Lehnert, Marilia Lirio, Nicolás Pérez de Olaguer, Heiko Ramm, Manish Sahu, Alexander Tack, Stefan Zachow, Tao Jiang, Xinjun Ma, Christoph Angerman, Xin Wang, Qingyue Wei, Kevin Brown, Matthias Wolf, Alexandre Kirszenberg, Élodie Puybareau, Alexander Valentinitsch, Markus Rempfler, Björn H. Menze, Jan S. Kirschke | VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images | arXiv, 2020 |
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2019 |
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| Felix Ambellan, Stefan Zachow, Christoph von Tycowicz | A Surface-Theoretic Approach for Statistical Shape Modeling | Proc. Medical Image Computing and Computer Assisted Intervention (MICCAI), Part IV, pp. 21-29, Vol.11767, Lecture Notes in Computer Science, 2019 (preprint available as ZIB-Report 19-20) |
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| Felix Ambellan, Stefan Zachow, Christoph von Tycowicz | An as-invariant-as-possible GL+(3)-based Statistical Shape Model | Proc. 7th MICCAI workshop on Mathematical Foundations of Computational Anatomy (MFCA), pp. 219-228, Vol.11846, Lecture Notes in Computer Science, 2019 (preprint available as ZIB-Report 19-46) |
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| Felix Ambellan, Hans Lamecker, Christoph von Tycowicz, Stefan Zachow | Statistical Shape Models - Understanding and Mastering Variation in Anatomy | Biomedical Visualisation, Paul M. Rea (Ed.), Springer Nature Switzerland AG, 1, pp. 67-84, 2019, ISBN: 978-3-030-19384-3, ISBN: 978-3-030-19385-0 (preprint available as ZIB-Report 19-13) |
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