提出大规模无监督脑部MRI配准挑战，推动深度学习成为医学影像基础模型。
Beyond the LUMIR challenge: The pathway to foundational registration models
- 基于4014例无标签T1 MRI，用自监督方式训练可生成生物合理形变场。
- 深度学习方法在590名受试者及跨疾病/协议/物种测试中表现最优且稳健。
- 适合关注医学影像配准、深度学习泛化能力的研究者和开发者。
医学图像挑战赛推动了领域发展，催生创新并设立新基准。图像配准作为神经影像学的基础任务，亦通过Learn2Reg项目不断进步。我们推出大规模无监督脑部MRI图像配准（LUMIR）挑战，作为新一代无监督脑部MRI配准基准。以往挑战依赖解剖标签图，而LUMIR提供4,014例未标注的T1加权MRI用于训练，通过自监督促使生物合理的形变建模。评估包含590名域内测试受试者及跨疾病群体、成像协议与物种的广泛零样本任务。深度学习方法持续达到最先进性能，生成解剖上合理的微分同胚形变场，优于多个领先优化方法，并对多数域偏移保持鲁棒。这些发现凸显深度学习在神经影像配准中的日益成熟，及其作为通用医学图像配准基础模型的潜力。
原文摘要 · Abstract (English)
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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