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（SAM）作为视觉分割的强效基础模型，可被适配用于医学图像分割。然而，医学成像数据通常包含隐私敏感信息，这使得通过集中式存储与共享方式训练基础模型面临挑战。迄今为止，在联邦学习框架内针对医学图像部署的专用基础模型仍十分匮乏，其分割性能及通信训练效率亦未得到充分探索。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（FedFMS），该框架包含联邦SAM（FedSAM）以及一种兼具通信与训练高效性的、配备医学SAM适配器的联邦SAM（FedMSA）。通过在多样数据集上开展全面实验，我们研究了集中式训练与联邦学习在FedFMS不同配置下的性能差异。实验表明，FedFMS在保持隐私性的同时，可取得与集中式训练方法相当的模型性能。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开源至 https://github.com/LIU-YUXI/FedFMS。