Medical image segmentation is crucial for clinical diagnosis. The Segmentation Anything Model (SAM) serves as a powerful foundation model for visual segmentation and can be adapted for medical image segmentation. However, medical imaging data typically contain privacy-sensitive information, making it challenging to train foundation models with centralized storage and sharing. To date, there are few foundation models tailored for medical image deployment within the federated learning framework, and the segmentation performance, as well as the efficiency of communication and training, remain unexplored. In response to these issues, we developed Federated Foundation models for Medical image Segmentation (FedFMS), which includes the Federated SAM (FedSAM) and a communication and training-efficient Federated SAM with Medical SAM Adapter (FedMSA). Comprehensive experiments on diverse datasets are conducted to investigate the performance disparities between centralized training and federated learning across various configurations of FedFMS. The experiments revealed that FedFMS could achieve performance comparable to models trained via centralized training methods while maintaining privacy. Furthermore, FedMSA demonstrated the potential to enhance communication and training efficiency. Our model implementation codes are available at https://github.com/LIU-YUXI/FedFMS.

翻译：医学图像分割对于临床诊断至关重要。分割一切模型（Segmentation Anything Model, SAM）作为一种强大的视觉分割基础模型，可适配于医学图像分割任务。然而，医学影像数据通常包含敏感隐私信息，使得采用集中式存储与共享方式训练基础模型面临挑战。迄今为止，鲜有专为联邦学习框架下医学图像部署定制的基础模型，其分割性能以及通信与训练效率仍有待探索。针对这些问题，我们开发了面向医学图像分割的联邦基础模型（Federated Foundation models for Medical image Segmentation, FedFMS），其中包含联邦SAM（FedSAM）以及一种通信与训练高效的、结合医学SAM适配器的联邦SAM（FedMSA）。我们在多样化数据集上进行了全面实验，以探究FedFMS在不同配置下集中式训练与联邦学习之间的性能差异。实验表明，FedFMS能够在保护隐私的同时，达到与集中式训练方法所训练模型相当的性能。此外，FedMSA展现出提升通信与训练效率的潜力。我们的模型实现代码已公开于 https://github.com/LIU-YUXI/FedFMS。