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| Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2505.24160 |
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| _version_ | 1866915750760939520 |
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| author | Chen, Junyu Wei, Shuwen Honkamaa, Joel Marttinen, Pekka Zhang, Hang Liu, Min Zhou, Yichao Tan, Zuopeng Wang, Zhuoyuan Wang, Yi Zhou, Hongchao Hu, Shunbo Zhang, Yi Tao, Qian Förner, Lukas Wendler, Thomas Jian, Bailiang Wiestler, Benedikt Hable, Tim Kim, Jin Ruan, Dan Madesta, Frederic Sentker, Thilo Heyer, Wiebke Zuo, Lianrui Dai, Yuwei Wu, Jing Prince, Jerry L. Bai, Harrison Du, Yong Liu, Yihao Hering, Alessa Dorent, Reuben Hansen, Lasse Heinrich, Mattias P. Carass, Aaron |
| author_facet | Chen, Junyu Wei, Shuwen Honkamaa, Joel Marttinen, Pekka Zhang, Hang Liu, Min Zhou, Yichao Tan, Zuopeng Wang, Zhuoyuan Wang, Yi Zhou, Hongchao Hu, Shunbo Zhang, Yi Tao, Qian Förner, Lukas Wendler, Thomas Jian, Bailiang Wiestler, Benedikt Hable, Tim Kim, Jin Ruan, Dan Madesta, Frederic Sentker, Thilo Heyer, Wiebke Zuo, Lianrui Dai, Yuwei Wu, Jing Prince, Jerry L. Bai, Harrison Du, Yong Liu, Yihao Hering, Alessa Dorent, Reuben Hansen, Lasse Heinrich, Mattias P. Carass, Aaron |
| contents | Medical image challenges have played a transformative role in advancing the field, catalyzing innovation and establishing new performance benchmarks. Image registration, a foundational task in neuroimaging, has similarly advanced through the Learn2Reg initiative. Building on this, we introduce the Large-scale Unsupervised Brain MRI Image Registration (LUMIR) challenge, a next-generation benchmark for unsupervised brain MRI registration. Previous challenges relied upon anatomical label maps, however LUMIR provides 4,014 unlabeled T1-weighted MRIs for training, encouraging biologically plausible deformation modeling through self-supervision. Evaluation includes 590 in-domain test subjects and extensive zero-shot tasks across disease populations, imaging protocols, and species. Deep learning methods consistently achieved state-of-the-art performance and produced anatomically plausible, diffeomorphic deformation fields. They outperformed several leading optimization-based methods and remained robust to most domain shifts. These findings highlight the growing maturity of deep learning in neuroimaging registration and its potential to serve as a foundation model for general-purpose medical image registration. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_24160 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Beyond the LUMIR challenge: The pathway to foundational registration models Chen, Junyu Wei, Shuwen Honkamaa, Joel Marttinen, Pekka Zhang, Hang Liu, Min Zhou, Yichao Tan, Zuopeng Wang, Zhuoyuan Wang, Yi Zhou, Hongchao Hu, Shunbo Zhang, Yi Tao, Qian Förner, Lukas Wendler, Thomas Jian, Bailiang Wiestler, Benedikt Hable, Tim Kim, Jin Ruan, Dan Madesta, Frederic Sentker, Thilo Heyer, Wiebke Zuo, Lianrui Dai, Yuwei Wu, Jing Prince, Jerry L. Bai, Harrison Du, Yong Liu, Yihao Hering, Alessa Dorent, Reuben Hansen, Lasse Heinrich, Mattias P. Carass, Aaron Image and Video Processing Computer Vision and Pattern Recognition Medical image challenges have played a transformative role in advancing the field, catalyzing innovation and establishing new performance benchmarks. Image registration, a foundational task in neuroimaging, has similarly advanced through the Learn2Reg initiative. Building on this, we introduce the Large-scale Unsupervised Brain MRI Image Registration (LUMIR) challenge, a next-generation benchmark for unsupervised brain MRI registration. Previous challenges relied upon anatomical label maps, however LUMIR provides 4,014 unlabeled T1-weighted MRIs for training, encouraging biologically plausible deformation modeling through self-supervision. Evaluation includes 590 in-domain test subjects and extensive zero-shot tasks across disease populations, imaging protocols, and species. Deep learning methods consistently achieved state-of-the-art performance and produced anatomically plausible, diffeomorphic deformation fields. They outperformed several leading optimization-based methods and remained robust to most domain shifts. These findings highlight the growing maturity of deep learning in neuroimaging registration and its potential to serve as a foundation model for general-purpose medical image registration. |
| title | Beyond the LUMIR challenge: The pathway to foundational registration models |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.24160 |