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Electrical Engineering and Systems Science > Image and Video Processing

arXiv:2408.15224 (eess)
[Submitted on 27 Aug 2024]

Title:SAM & SAM 2 in 3D Slicer: SegmentWithSAM Extension for Annotating Medical Images

Authors:Zafer Yildiz, Yuwen Chen, Maciej A. Mazurowski
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Abstract: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 this https URL and can be easily installed directly from the Extension Manager of 3D Slicer as well.
Comments: Future work: support for box and mask inputs for the video predictor of SAM 2
Subjects: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Software Engineering (cs.SE)
Cite as: arXiv:2408.15224 [eess.IV]
  (or arXiv:2408.15224v1 [eess.IV] for this version)
  https://doi.org/10.48550/arXiv.2408.15224

Submission history

From: Zafer Yildiz [view email]
[v1] Tue, 27 Aug 2024 17:39:33 UTC (1,416 KB)
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