The unprecedented developments in segmentation foundational models have become a dominant force in the field of computer vision, introducing a multitude of previously unexplored capabilities in a wide range of natural images and videos. Specifically, the Segment Anything Model (SAM) signifies a noteworthy expansion of the prompt-driven paradigm into the domain of image segmentation. The recent introduction of SAM2 effectively extends the original SAM to a streaming fashion and demonstrates strong performance in video segmentation. However, due to the substantial distinctions between natural and medical images, the effectiveness of these models on biomedical images and videos is still under exploration. This paper presents an overview of recent efforts in applying and adapting SAM2 to biomedical images and videos. The findings indicate that while SAM2 shows promise in reducing annotation burdens and enabling zero-shot segmentation, its performance varies across different datasets and tasks. Addressing the domain gap between natural and medical images through adaptation and fine-tuning is essential to fully unleash SAM2's potential in clinical applications. To support ongoing research endeavors, we maintain an active repository that contains up-to-date SAM & SAM2-related papers and projects at https://github.com/YichiZhang98/SAM4MIS.

翻译：分割基础模型前所未有的发展已成为计算机视觉领域的主导力量，为广泛的自然图像和视频引入了大量先前未曾探索的能力。具体而言，Segment Anything Model (SAM)标志着提示驱动范式向图像分割领域的重要扩展。近期推出的SAM2有效地将原始SAM扩展至流式处理模式，并在视频分割中展现出强大的性能。然而，由于自然图像与医学图像之间存在显著差异，这些模型在生物医学图像和视频上的有效性仍在探索之中。本文综述了近期在生物医学图像与视频中应用和适配SAM2的相关研究进展。研究结果表明，尽管SAM2在减轻标注负担和实现零样本分割方面展现出潜力，但其性能在不同数据集和任务中存在差异。通过适配与微调来解决自然图像与医学图像之间的领域差距，对于在临床应用中充分释放SAM2的潜力至关重要。为支持持续的研究工作，我们在https://github.com/YichiZhang98/SAM4MIS 维护了一个包含最新SAM与SAM2相关论文及项目的动态资源库。