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

Zafer Yildiz, Yuwen Chen, Maciej A. Mazurowski
8/27/2024
eess.IVcs.CVcs.SE

Official code: mazurowski-lab/SlicerSegmentWithSAM (73 stars, Apache-2.0, last commit 2025-06-02).

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 https://github.com/mazurowski-lab/SlicerSegmentWithSAM and can be easily installed directly from the Extension Manager of 3D Slicer as well.

View on arXivView PDF

Code Implementations(1)

mazurowski-lab/SlicerSegmentWithSAMOfficial100%
Apache-2.0

3D Slicer extension for Segment Anything Model (SAM) developed by Meta

738Cuda, CMakeJul 10, 20231 years agoApache-2.0
3d-slicer3d-slicer-extensionmedical-image-segmentationsegment-anything-modelsegmentation

Similar Papers

Interactive 3D Medical Image Segmentation with SAM 2

87% match1 repo392024

Unleashing the Potential of SAM2 for Biomedical Images and Videos: A Survey

85% match1 repo1.1k2024

Rethinking Few-Shot Medical Image Segmentation by SAM2: A Training-Free Framework with Augmentative Prompting and Dynamic Matching

85% match2025

Lightweight Method for Interactive 3D Medical Image Segmentation with Multi-Round Result Fusion

85% match1 repo12024

MedSAM2: Segment Anything in 3D Medical Images and Videos

85% match1 repo7272025

Efficient Universal Models for Medical Image Segmentation via Weakly Supervised In-Context Learning

84% match1 repo22025

Cite this paper

@article{yildiz2024slicer,
  title  = {SAM & SAM 2 in 3D Slicer: SegmentWithSAM Extension for Annotating Medical Images},
  author = {Zafer Yildiz and Yuwen Chen and Maciej A. Mazurowski},
  year   = {2024},
  eprint = {2408.15224},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2408.15224v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2408.15224.svg)](https://codeofpaper.com/paper/2408.15224)

Discussion