1 code implementation • ICCV 2023 • Martin J. Menten, Johannes C. Paetzold, Veronika A. Zimmer, Suprosanna Shit, Ivan Ezhov, Robbie Holland, Monika Probst, Julia A. Schnabel, Daniel Rueckert
Finally, we demonstrate the utility of our algorithm by integrating it with two medical image processing applications that use gradient-based optimization: deep-learning-based blood vessel segmentation, and multimodal registration of the mandible in computed tomography and magnetic resonance images.
1 code implementation • 31 Aug 2023 • Chinmay Prabhakar, Hongwei Bran Li, Johannes C. Paetzold, Timo Loehr, Chen Niu, Mark Mühlau, Daniel Rueckert, Benedikt Wiestler, Bjoern Menze
We propose a two-stage MS inflammatory disease activity prediction approach.
1 code implementation • 19 Jun 2023 • Linus Kreitner, Johannes C. Paetzold, Nikolaus Rauch, Chen Chen, Ahmed M. Hagag, Alaa E. Fayed, Sobha Sivaprasad, Sebastian Rausch, Julian Weichsel, Bjoern H. Menze, Matthias Harders, Benjamin Knier, Daniel Rueckert, Martin J. Menten
Optical coherence tomography angiography (OCTA) is a non-invasive imaging modality that can acquire high-resolution volumes of the retinal vasculature and aid the diagnosis of ocular, neurological and cardiac diseases.
no code implementations • 24 Apr 2023 • Leon Mächler, Ivan Ezhov, Suprosanna Shit, Johannes C. Paetzold
Another key challenge was the vastly varying size of data samples per center.
no code implementations • 25 Mar 2023 • Bastian Wittmann, Johannes C. Paetzold, Chinmay Prabhakar, Daniel Rueckert, Bjoern Menze
In this work, we focus on link prediction for flow-driven spatial networks, which are embedded in a Euclidean space and relate to physical exchange and transportation processes (e. g., blood flow in vessels or traffic flow in road networks).
no code implementations • 11 Jan 2023 • Robbie Holland, Oliver Leingang, Christopher Holmes, Philipp Anders, Rebecca Kaye, Sophie Riedl, Johannes C. Paetzold, Ivan Ezhov, Hrvoje Bogunović, Ursula Schmidt-Erfurth, Lars Fritsche, Hendrik P. N. Scholl, Sobha Sivaprasad, Andrew J. Lotery, Daniel Rueckert, Martin J. Menten
Age-related macular degeneration (AMD) is the leading cause of blindness in the elderly.
1 code implementation • 28 Nov 2022 • Nico Stucki, Johannes C. Paetzold, Suprosanna Shit, Bjoern Menze, Ulrich Bauer
In this work, we propose the first topologically and feature-wise accurate metric and loss function for supervised image segmentation, which we term Betti matching.
no code implementations • 28 Oct 2022 • Linus Kreitner, Ivan Ezhov, Daniel Rueckert, Johannes C. Paetzold, Martin J. Menten
Recent studies suggest that early stages of diabetic retinopathy (DR) can be diagnosed by monitoring vascular changes in the deep vascular complex.
1 code implementation • 22 Jul 2022 • Martin J. Menten, Johannes C. Paetzold, Alina Dima, Bjoern H. Menze, Benjamin Knier, Daniel Rueckert
Encouraged by our method's competitive quantitative and superior qualitative performance, we believe that it constitutes a versatile tool to advance the quantitative analysis of OCTA images.
no code implementations • 5 May 2022 • Dmitrii Usynin, Helena Klause, Johannes C. Paetzold, Daniel Rueckert, Georgios Kaissis
In federated learning for medical image analysis, the safety of the learning protocol is paramount.
no code implementations • 17 Mar 2022 • Tamara T. Mueller, Dmitrii Usynin, Johannes C. Paetzold, Daniel Rueckert, Georgios Kaissis
In this work, we study the applications of differential privacy (DP) in the context of graph-structured data.
1 code implementation • 5 Feb 2022 • Tamara T. Mueller, Johannes C. Paetzold, Chinmay Prabhakar, Dmitrii Usynin, Daniel Rueckert, Georgios Kaissis
In this work, we introduce differential privacy for graph-level classification, one of the key applications of machine learning on graphs.
1 code implementation • 24 Dec 2021 • Lucas Fidon, Suprosanna Shit, Ivan Ezhov, Johannes C. Paetzold, Sébastien Ourselin, Tom Vercauteren
Importantly, we explore the inclusion of a transformer in the bottleneck of the U-Net architecture.
no code implementations • 16 Nov 2021 • Leon Mächler, Ivan Ezhov, Florian Kofler, Suprosanna Shit, Johannes C. Paetzold, Timo Loehr, Benedikt Wiestler, Bjoern Menze
We propose a simple new aggregation strategy for federated learning that won the MICCAI Federated Tumor Segmentation Challenge 2021 (FETS), the first ever challenge on Federated Learning in the Machine Learning community.
no code implementations • 3 Sep 2021 • Suprosanna Shit, Ivan Ezhov, Leon Mächler, Abinav R., Jana Lipkova, Johannes C. Paetzold, Florian Kofler, Marie Piraud, Bjoern H. Menze
In this paper, we propose a neural solver to learn an optimal iterative scheme in a data-driven fashion for any class of PDEs.
1 code implementation • 30 Aug 2021 • Johannes C. Paetzold, Julian McGinnis, Suprosanna Shit, Ivan Ezhov, Paul Büschl, Chinmay Prabhakar, Mihail I. Todorov, Anjany Sekuboyina, Georgios Kaissis, Ali Ertürk, Stephan Günnemann, Bjoern H. Menze
Moreover, we benchmark numerous state-of-the-art graph learning algorithms on the biologically relevant tasks of vessel prediction and vessel classification using the introduced vessel graph dataset.
1 code implementation • CVPR 2021 • Suprosanna Shit, Johannes C. Paetzold, Anjany Sekuboyina, Ivan Ezhov, Alexander Unger, Andrey Zhylka, Josien P. W. Pluim, Ulrich Bauer, Bjoern H. Menze
Accurate segmentation of tubular, network-like structures, such as vessels, neurons, or roads, is relevant to many fields of research.
no code implementations • 22 Apr 2021 • Izabela Horvath, Johannes C. Paetzold, Oliver Schoppe, Rami Al-Maskari, Ivan Ezhov, Suprosanna Shit, Hongwei Li, Ali Ertuerk, Bjoern H. Menze
Novel multimodal imaging methods are capable of generating extensive, super high resolution datasets for preclinical research.
no code implementations • 4 Mar 2021 • Fabian Balsiger, Alain Jungo, Naren Akash R J, Jianan Chen, Ivan Ezhov, Shengnan Liu, Jun Ma, Johannes C. Paetzold, Vishva Saravanan R, Anjany Sekuboyina, Suprosanna Shit, Yannick Suter, Moshood Yekini, Guodong Zeng, Markus Rempfler
With this growth, however, come new challenges for the community.
1 code implementation • 29 Oct 2020 • Kelly Payette, Priscille de Dumast, Hamza Kebiri, Ivan Ezhov, Johannes C. Paetzold, Suprosanna Shit, Asim Iqbal, Romesa Khan, Raimund Kottke, Patrice Grehten, Hui Ji, Levente Lanczi, Marianna Nagy, Monika Beresova, Thi Dao Nguyen, Giancarlo Natalucci, Theofanis Karayannis, Bjoern Menze, Meritxell Bach Cuadra, Andras Jakab
It is critical to quantitatively analyse the developing human fetal brain in order to fully understand neurodevelopment in both normal fetuses and those with congenital disorders.
no code implementations • 13 Jul 2020 • Amirhossein Bayat, Anjany Sekuboyina, Johannes C. Paetzold, Christian Payer, Darko Stern, Martin Urschler, Jan S. Kirschke, Bjoern H. Menze
The treatment of degenerative spinal disorders requires an understanding of the individual spinal anatomy and curvature in 3D.
no code implementations • 10 Jul 2020 • Stefan Gerl, Johannes C. Paetzold, Hailong He, Ivan Ezhov, Suprosanna Shit, Florian Kofler, Amirhossein Bayat, Giles Tetteh, Vasilis Ntziachristos, Bjoern Menze
Raster-scan optoacoustic mesoscopy (RSOM) is a powerful, non-invasive optical imaging technique for functional, anatomical, and molecular skin and tissue analysis.
1 code implementation • 22 Apr 2020 • Ahmad B Qasim, Ivan Ezhov, Suprosanna Shit, Oliver Schoppe, Johannes C. Paetzold, Anjany Sekuboyina, Florian Kofler, Jana Lipkova, Hongwei Li, Bjoern Menze
Exploiting learning algorithms under scarce data regimes is a limitation and a reality of the medical imaging field.
3 code implementations • 16 Mar 2020 • Suprosanna Shit, Johannes C. Paetzold, Anjany Sekuboyina, Ivan Ezhov, Alexander Unger, Andrey Zhylka, Josien P. W. Pluim, Ulrich Bauer, Bjoern H. Menze
Accurate segmentation of tubular, network-like structures, such as vessels, neurons, or roads, is relevant to many fields of research.
2 code implementations • 24 Jan 2020 • 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
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/).
1 code implementation • 29 Apr 2019 • Hongwei Li, Johannes C. Paetzold, Anjany Sekuboyina, Florian Kofler, Jian-Guo Zhang, Jan S. Kirschke, Benedikt Wiestler, Bjoern Menze
Synthesizing MR imaging sequences is highly relevant in clinical practice, as single sequences are often missing or are of poor quality (e. g. due to motion).
6 code implementations • 13 Jan 2019 • Patrick Bilic, Patrick Christ, Hongwei Bran Li, Eugene Vorontsov, Avi Ben-Cohen, Georgios Kaissis, Adi Szeskin, Colin Jacobs, Gabriel Efrain Humpire Mamani, Gabriel Chartrand, Fabian Lohöfer, Julian Walter Holch, Wieland Sommer, Felix Hofmann, Alexandre Hostettler, Naama Lev-Cohain, Michal Drozdzal, Michal Marianne Amitai, Refael Vivantik, Jacob Sosna, Ivan Ezhov, Anjany Sekuboyina, Fernando Navarro, Florian Kofler, Johannes C. Paetzold, Suprosanna Shit, Xiaobin Hu, Jana Lipková, Markus Rempfler, Marie Piraud, Jan Kirschke, Benedikt Wiestler, Zhiheng Zhang, Christian Hülsemeyer, Marcel Beetz, Florian Ettlinger, Michela Antonelli, Woong Bae, Míriam Bellver, Lei Bi, Hao Chen, Grzegorz Chlebus, Erik B. Dam, Qi Dou, Chi-Wing Fu, Bogdan Georgescu, Xavier Giró-i-Nieto, Felix Gruen, Xu Han, Pheng-Ann Heng, Jürgen Hesser, Jan Hendrik Moltz, Christian Igel, Fabian Isensee, Paul Jäger, Fucang Jia, Krishna Chaitanya Kaluva, Mahendra Khened, Ildoo Kim, Jae-Hun Kim, Sungwoong Kim, Simon Kohl, Tomasz Konopczynski, Avinash Kori, Ganapathy Krishnamurthi, Fan Li, Hongchao Li, Junbo Li, Xiaomeng Li, John Lowengrub, Jun Ma, Klaus Maier-Hein, Kevis-Kokitsi Maninis, Hans Meine, Dorit Merhof, Akshay Pai, Mathias Perslev, Jens Petersen, Jordi Pont-Tuset, Jin Qi, Xiaojuan Qi, Oliver Rippel, Karsten Roth, Ignacio Sarasua, Andrea Schenk, Zengming Shen, Jordi Torres, Christian Wachinger, Chunliang Wang, Leon Weninger, Jianrong Wu, Daguang Xu, Xiaoping Yang, Simon Chun-Ho Yu, Yading Yuan, Miao Yu, Liping Zhang, Jorge Cardoso, Spyridon Bakas, Rickmer Braren, Volker Heinemann, Christopher Pal, An Tang, Samuel Kadoury, Luc Soler, Bram van Ginneken, Hayit Greenspan, Leo Joskowicz, Bjoern Menze
In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018.