Creating annotations for 3D medical data is time-consuming and often requires highly specialized expertise. Various tools have been implemented to aid this process. Segment Anything Model 2 (SAM 2) offers a general-purpose prompt-based segmentation algorithm designed to annotate videos. In this paper, we adapt this model to the annotation of 3D medical images and offer our implementation in the form of an extension to the popular annotation software: 3D Slicer. Our extension allows users to place point prompts on 2D slices to generate annotation masks and propagate these annotations across entire volumes in either single-directional or bi-directional manners. Our code is publicly available on https://github.com/mazurowski-lab/SlicerSegmentWithSAM and can be easily installed directly from the Extension Manager of 3D Slicer as well.

本研究解决了3D医学数据注释耗时且需要专业技术的难题。我们将通用的提示式分割算法SAM 2适应于3D医学图像注释，并通过3D Slicer软件提供了实现，使用户能够在2D切片上放置点提示，以生成注释掩模并在整个体积中传播这些注释。该扩展可提高医学图像的注释效率，具有重要的应用潜力。

SAM SAM 2 在 3D Slicer 中：用于医学图像注释的 SegmentWithSAM 扩展