Year: 2025
Type: article
Abstract: Medical image registration is critical for clinical applications, and fair benchmarking of different methods is essential for monitoring ongoing progress in the field. To date, the Learn2Reg 2020-2023 challenges have released several complementary datasets and established metrics for evaluations. Building on this foundation, the 2024 edition expands the challenge’s scope to cover a wider range of registration scenarios, particularly in terms of modality diversity and task complexity, by introducing three new tasks, including large-scale multi-modal registration and unsupervised inter-subject brain registration, as well as the first microscopy-focused benchmark within Learn2Reg. The new datasets also inspired new method developments, including invertibility constraints, pyramid features, keypoints alignment and instance optimisation.
Authors: Lasse Hansen, Wiebke Heyer, Christoph Großbröhmer, Frederic Madesta, Thilo Sentker, Wang Jiazheng, Yuxi Zhang, Hang Zhang, Min Liu, Junyi Wang, Xi Zhu, Yuhua Li, Liwen Wang, Daniil Morozov, Nazim Haouchine, Joel Honkamaa, Pekka Marttinen, Yichao Zhou, Zuopeng Tan, Zhuoyuan Wang, Yi Wang, Hongchao Zhou, Shunbo Hu, Yi Zhang, Qian Tao, Lukas Förner, Thomas Wendler, Bailiang Jian, Christian Wachinger, Jin Kim, Dan Ruan, Marek Wodzinski, Henning Müller, Tony C.W. Mok, Xi Jia, Jinming Duan, Mikael Brudfors, Seyed-Ahmad Ahmadi, Yunzheng Zhu, William Hsu, Tina Kapur, William M. Wells, Alexandra Golby, Aaron Carass, Harrison Bai, Yihao Liu, Perrine Paul-Gilloteaux, Joakim Lindblad, Nataša Sladoje, Andreas Walter, Junyu Chen, Reuben Dorent, Alessa Hering, Mattias P. Heinrich
Institutions: University of Lübeck, University Medical Center Hamburg-Eppendorf, Hunan University, Delft University of Technology, Cornell University, University of Electronic Science and Technology of China, Tianjin University, Brigham and Women's Hospital, Harvard University, Technical University of Munich, Aalto University, Shenzhen University, Linyi University, University Hospital Augsburg, TUM Klinikum, University of California, Los Angeles, Sano – Centre for Computational Personalised Medicine International Research Foundation, AGH University of Krakow, University of Geneva, HES-SO University of Applied Sciences and Arts Western Switzerland, Hong Kong University of Science and Technology, University of Birmingham, Johns Hopkins University, Vanderbilt University, Centre National de la Recherche Scientifique, Inserm, Centre Hospitalier Universitaire de Nantes, UMS BioCore, Structure Fédérative de Recherche Bonamy, Nantes Université, Uppsala University, Radboud University Medical Center
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Funder: Francis Crick Institute
OpenAlex ID: W4417539436