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Main Authors: 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
Format: Preprint
Published: 2025
Subjects:
Image and Video Processing
Computer Vision and Pattern Recognition
Online Access:https://arxiv.org/abs/2505.24160
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Table of 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.

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