LUMIR Challenge: Deep learning models achieve robust, diffeomorphic brain MRI registration across domain shifts

In the LUMIR challenge, deep learning registration methods outperformed optimization-based approaches on 590 test brain MRIs, producing robust, diffeomorphic deformations that generalized to unseen diseases, protocols, and species in zero-shot tasks.

• Design: Benchmark challenge; 4014 unlabeled T1-weighted MRIs for unsupervised training with self-supervised learning, evaluated on 590 in-domain and extensive zero-shot test sets.
• Deep learning methods consistently state-of-the-art, remaining robust to most domain shifts and outperforming leading optimization-based methods (exact quantitative metrics not reported in source).
• Limitation: Challenge-based evaluation only; no discussion of external, real-world clinical validation or workflow integration.

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