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.

翻译：随着Segment Anything Model（SAM）的提出，针对医学图像分割任务对SAM进行微调已变得流行。然而，由于SAM模型规模庞大且自然图像与医学图像之间存在显著领域差异，基于微调的策略成本高昂，并存在不稳定性、特征损伤及灾难性遗忘等潜在风险。此外，部分通过微调策略将SAM迁移至特定领域医学图像分割的方法会削弱模型的提示能力，严重限制其应用场景。本文提出了一种自动提示模块（APM），该模块能够为目标域中的SAM基础模型提供欧几里得自适应提示。实验表明，此类自适应提示可显著提升SAM在医学图像分割中无需微调的性能。此外，我们提出了一种名为增量模式移位（IPS）的新型非侵入式方法，使SAM能够适应特定医学领域。实验结果显示，IPS使SAM无需微调即可在医学图像分割中达到最先进或具有竞争力的性能。通过结合这两种方法，我们提出了ProMISe——一种端到端无需微调的可提示医学图像分割框架。实验证明，单独或联合使用我们的方法均能在低成本模式移位中取得令人满意的效果，且所有SAM参数保持冻结状态。