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Main Authors:	Hansen, Lasse, Heyer, Wiebke, Großbröhmer, Christoph, Madesta, Frederic, Sentker, Thilo, Jiazheng, Wang, Zhang, Yuxi, Zhang, Hang, Liu, Min, Wang, Junyi, Zhu, Xi, Li, Yuhua, Wang, Liwen, Morozov, Daniil, Haouchine, Nazim, Honkamaa, Joel, Marttinen, Pekka, Zhou, Yichao, Tan, Zuopeng, Wang, Zhuoyuan, Wang, Yi, Zhou, Hongchao, Hu, Shunbo, Zhang, Yi, Tao, Qian, Förner, Lukas, Wendler, Thomas, Jian, Bailiang, Wachinger, Christian, Kim, Jin, Ruan, Dan, Wodzinski, Marek, Müller, Henning, Mok, Tony C. W., Jia, Xi, Duan, Jinming, Brudfors, Mikael, Ahmadi, Seyed-Ahmad, Zhu, Yunzheng, Hsu, William, Kapur, Tina, Wells, William M., Golby, Alexandra, Carass, Aaron, Bai, Harrison, Liu, Yihao, Paul-Gilloteaux, Perrine, Lindblad, Joakim, Sladoje, Nataša, Walter, Andreas, Chen, Junyu, Dorent, Reuben, Hering, Alessa, Heinrich, Mattias P.
Format:	Preprint
Published:	2025
Subjects:	Image and Video Processing Computer Vision and Pattern Recognition
Online Access:	https://arxiv.org/abs/2509.01217
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_version_	1866909982977425408
author	Hansen, Lasse Heyer, Wiebke Großbröhmer, Christoph Madesta, Frederic Sentker, Thilo Jiazheng, Wang Zhang, Yuxi Zhang, Hang Liu, Min Wang, Junyi Zhu, Xi Li, Yuhua Wang, Liwen Morozov, Daniil Haouchine, Nazim Honkamaa, Joel Marttinen, Pekka Zhou, Yichao Tan, Zuopeng Wang, Zhuoyuan Wang, Yi Zhou, Hongchao Hu, Shunbo Zhang, Yi Tao, Qian Förner, Lukas Wendler, Thomas Jian, Bailiang Wachinger, Christian Kim, Jin Ruan, Dan Wodzinski, Marek Müller, Henning Mok, Tony C. W. Jia, Xi Duan, Jinming Brudfors, Mikael Ahmadi, Seyed-Ahmad Zhu, Yunzheng Hsu, William Kapur, Tina Wells, William M. Golby, Alexandra Carass, Aaron Bai, Harrison Liu, Yihao Paul-Gilloteaux, Perrine Lindblad, Joakim Sladoje, Nataša Walter, Andreas Chen, Junyu Dorent, Reuben Hering, Alessa Heinrich, Mattias P.
author_facet	Hansen, Lasse Heyer, Wiebke Großbröhmer, Christoph Madesta, Frederic Sentker, Thilo Jiazheng, Wang Zhang, Yuxi Zhang, Hang Liu, Min Wang, Junyi Zhu, Xi Li, Yuhua Wang, Liwen Morozov, Daniil Haouchine, Nazim Honkamaa, Joel Marttinen, Pekka Zhou, Yichao Tan, Zuopeng Wang, Zhuoyuan Wang, Yi Zhou, Hongchao Hu, Shunbo Zhang, Yi Tao, Qian Förner, Lukas Wendler, Thomas Jian, Bailiang Wachinger, Christian Kim, Jin Ruan, Dan Wodzinski, Marek Müller, Henning Mok, Tony C. W. Jia, Xi Duan, Jinming Brudfors, Mikael Ahmadi, Seyed-Ahmad Zhu, Yunzheng Hsu, William Kapur, Tina Wells, William M. Golby, Alexandra Carass, Aaron Bai, Harrison Liu, Yihao Paul-Gilloteaux, Perrine Lindblad, Joakim Sladoje, Nataša Walter, Andreas Chen, Junyu Dorent, Reuben Hering, Alessa Heinrich, Mattias P.
contents	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. Visit Learn2Reg at https://learn2reg.grand-challenge.org.
format	Preprint
id	arxiv_https___arxiv_org_abs_2509_01217
institution	arXiv
publishDate	2025
record_format	arxiv
spellingShingle	Learn2Reg 2024: New Benchmark Datasets Driving Progress on New Challenges Hansen, Lasse Heyer, Wiebke Großbröhmer, Christoph Madesta, Frederic Sentker, Thilo Jiazheng, Wang Zhang, Yuxi Zhang, Hang Liu, Min Wang, Junyi Zhu, Xi Li, Yuhua Wang, Liwen Morozov, Daniil Haouchine, Nazim Honkamaa, Joel Marttinen, Pekka Zhou, Yichao Tan, Zuopeng Wang, Zhuoyuan Wang, Yi Zhou, Hongchao Hu, Shunbo Zhang, Yi Tao, Qian Förner, Lukas Wendler, Thomas Jian, Bailiang Wachinger, Christian Kim, Jin Ruan, Dan Wodzinski, Marek Müller, Henning Mok, Tony C. W. Jia, Xi Duan, Jinming Brudfors, Mikael Ahmadi, Seyed-Ahmad Zhu, Yunzheng Hsu, William Kapur, Tina Wells, William M. Golby, Alexandra Carass, Aaron Bai, Harrison Liu, Yihao Paul-Gilloteaux, Perrine Lindblad, Joakim Sladoje, Nataša Walter, Andreas Chen, Junyu Dorent, Reuben Hering, Alessa Heinrich, Mattias P. Image and Video Processing Computer Vision and Pattern Recognition 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. Visit Learn2Reg at https://learn2reg.grand-challenge.org.
title	Learn2Reg 2024: New Benchmark Datasets Driving Progress on New Challenges
topic	Image and Video Processing Computer Vision and Pattern Recognition
url	https://arxiv.org/abs/2509.01217

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