Due to the inherent flexibility of prompting, foundation models have emerged as the predominant force in the fields of natural language processing and computer vision. The recent introduction of the Segment Anything Model (SAM) signifies a noteworthy expansion of the prompt-driven paradigm into the domain of image segmentation, thereby introducing a plethora of previously unexplored capabilities. However, the viability of its application to medical image segmentation remains uncertain, given the substantial distinctions between natural and medical images. In this work, we provide a comprehensive overview of recent endeavors aimed at extending the efficacy of SAM to medical image segmentation tasks, encompassing both empirical benchmarking and methodological adaptations. Additionally, we explore potential avenues for future research directions in SAM’s role within medical image segmentation. While direct application of SAM to medical image segmentation does not yield satisfactory performance on multi-modal and multi-target medical datasets so far, numerous insights gleaned from these efforts serve as valuable guidance for shaping the trajectory of foundational models in the realm of medical image analysis. To support ongoing research endeavors, we maintain an active repository that contains an up-to-date paper list and a succinct summary of open-source projects at .

中文翻译：

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用于医学图像分割的分割任何模型：当前应用和未来方向

由于提示固有的灵活性，基础模型已成为自然语言处理和计算机视觉领域的主导力量。最近推出的分段任意模型 (SAM) 标志着提示驱动范式在图像分割领域的显着扩展，从而引入了大量以前未开发的功能。然而，考虑到自然图像和医学图像之间的巨大区别，其应用于医学图像分割的可行性仍然不确定。在这项工作中，我们全面概述了近期旨在将 SAM 的功效扩展到医学图像分割任务的努力，包括经验基准测试和方法适应。此外，我们还探讨了 SAM 在医学图像分割中的作用的未来研究方向的潜在途径。虽然迄今为止，将 SAM 直接应用于医学图像分割并没有在多模态和多目标医学数据集上产生令人满意的性能，但从这些工作中收集到的大量见解可以为塑造医学图像领域基础模型的轨迹提供宝贵的指导。分析。为了支持正在进行的研究工作，我们维护一个活跃的存储库，其中包含最新的论文列表和开源项目的简洁摘要，网址为 。