SAM & SAM 2 in 3D Slicer: SegmentWithSAM Extension for Annotating Medical Images
This work addresses the problem of efficient medical image annotation for clinicians and researchers, but it is incremental as it applies an existing model to a new domain.
The paper tackles the time-consuming and expertise-intensive process of annotating 3D medical images by adapting the Segment Anything Model 2 (SAM 2) for this domain, resulting in an extension for 3D Slicer that enables users to generate and propagate annotations across volumes using point prompts.
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.