With the proposal of the Segment Anything Model (SAM), fine-tuning SAM for medical image segmentation (MIS) has become popular. However, due to the large size of the SAM model and the significant domain gap between natural and medical images, fine-tuning-based strategies are costly with potential risk of instability, feature damage and catastrophic forgetting. Furthermore, some methods of transferring SAM to a domain-specific MIS through fine-tuning strategies disable the model's prompting capability, severely limiting its utilization scenarios. In this paper, we propose an Auto-Prompting Module (APM), which provides SAM-based foundation model with Euclidean adaptive prompts in the target domain. Our experiments demonstrate that such adaptive prompts significantly improve SAM's non-fine-tuned performance in MIS. In addition, we propose a novel non-invasive method called Incremental Pattern Shifting (IPS) to adapt SAM to specific medical domains. Experimental results show that the IPS enables SAM to achieve state-of-the-art or competitive performance in MIS without the need for fine-tuning. By coupling these two methods, we propose ProMISe, an end-to-end non-fine-tuned framework for Promptable Medical Image Segmentation. Our experiments demonstrate that both using our methods individually or in combination achieves satisfactory performance in low-cost pattern shifting, with all of SAM's parameters frozen.

提出了Auto-Prompting Module（APM）和Incremental Pattern Shifting（IPS）两种方法，其中APM通过提供欧几里得自适应提示显著提高了Segment Anything Model (SAM) 在医学图像分割中的性能，而IPS则使SAM在特定医学领域中达到了与fine-tuning相媲美的最先进或有竞争力的性能，将这两种方法结合起来构建了一个冻结SAM所有参数的端到端、非fine-tuning的医学图像分割框架ProMISe。

ProMISe：使用SAM进行可提示的医学图像分割