Learn2Reg 2024: New Benchmark Datasets Driving Progress on New Challenges
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, 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
Published: 2025/9/1
Abstract
Medical image registration is critical for clinical applications, and fair benchmarking of different methods is essential for monitoring ongoing progress. To date, the Learn2Reg 2020-2023 challenges have released several complementary datasets and established metrics for evaluations. However, these editions did not capture all aspects of the registration problem, particularly in terms of modality diversity and task complexity. To address these limitations, the 2024 edition introduces 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.