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.

翻译：医学影像挑战赛在推动该领域发展方面发挥了变革性作用，促进了创新并建立了新的性能基准。图像配准作为神经影像学的基础任务，同样通过Learn2Reg计划取得了进展。在此基础上，我们推出了大规模无监督脑部MRI图像配准（LUMIR）挑战赛，这是新一代无监督脑部MRI配准的基准。以往的挑战赛依赖于解剖标签图，而LUMIR提供了4,014个未标注的T1加权MRI图像用于训练，通过自监督学习鼓励建立生物学上合理的形变模型。评估包括590个域内测试对象以及跨疾病人群、成像协议和物种的广泛零样本任务。深度学习方法持续实现了最先进的性能，并产生了解剖学上合理、可微分的形变场。它们超越了多种领先的基于优化的方法，并在大多数域偏移情况下保持稳健。这些发现凸显了深度学习在神经影像配准领域的日益成熟及其作为通用医学图像配准基础模型的潜力。