Due to the flexibility of prompting, foundation models have become the dominant force in the domains of natural language processing and image generation. With the recent introduction of the Segment Anything Model (SAM), the prompt-driven paradigm has entered the realm of image segmentation, bringing with a range of previously unexplored capabilities. However, it remains unclear whether it can be applicable to medical image segmentation due to the significant differences between natural images and medical images.In this work, we summarize recent efforts to extend the success of SAM to medical image segmentation tasks, including both empirical benchmarking and methodological adaptations, and discuss potential future directions for SAM in medical image segmentation. Although directly applying SAM to medical image segmentation cannot obtain satisfying performance on multi-modal and multi-target medical datasets, many insights are drawn to guide future research to develop foundation models for medical image analysis. To facilitate future research, we maintain an active repository that contains up-to-date paper list and open-source project summary at https://github.com/YichiZhang98/SAM4MIS.

翻译：由于提示机制的灵活性，基础模型已成为自然语言处理和图像生成领域的主导力量。随着分割一切模型（SAM）的近期引入，提示驱动范式已进入图像分割领域，带来了一系列先前未被探索的能力。然而，由于自然图像与医学图像之间存在显著差异，SAM是否能适用于医学图像分割仍不明确。本文总结了近期将SAM成功拓展至医学图像分割任务的相关工作，包括经验性基准测试和方法适应性调整，并探讨了SAM在医学图像分割中的潜在未来方向。尽管直接将SAM应用于医学图像分割无法在多模态、多目标医学数据集上获得令人满意的性能，但本文提炼出多项启发，以指导未来开发医学图像分析基础模型的研究。为促进后续研究，我们维护了一个活跃的代码库，其中包含最新的论文列表和开源项目总结，链接为https://github.com/YichiZhang98/SAM4MIS。