Deep learning has revolutionized medical image registration by achieving unprecedented speeds, yet its clinical application is hindered by a limited ability to generalize beyond the training domain, a critical weakness given the typically small scale of medical datasets. In this paper, we introduce FMIR, a foundation model-based registration framework that overcomes this limitation.Combining a foundation model-based feature encoder for extracting anatomical structures with a general registration head, and trained with a channel regularization strategy on just a single dataset, FMIR achieves state-of-the-art(SOTA) in-domain performance while maintaining robust registration on out-of-domain images.Our approach demonstrates a viable path toward building generalizable medical imaging foundation models with limited resources. The code is available at https://github.com/Monday0328/FMIR.git.

翻译：深度学习通过实现前所未有的速度彻底改变了医学图像配准领域，但其临床应用受到泛化能力有限的制约——这一关键缺陷在医学数据集通常规模较小的情况下尤为突出。本文提出FMIR，一种基于基础模型的配准框架，以克服这一局限。该框架结合了用于提取解剖结构的基于基础模型的特征编码器与通用配准头，并仅需在单个数据集上通过通道正则化策略进行训练，即可在保持域外图像鲁棒配准能力的同时，实现领域内最先进的性能。我们的方法展示了在有限资源下构建可泛化医学影像基础模型的可行路径。代码发布于 https://github.com/Monday0328/FMIR.git。