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

Autor:	Yildiz, Zafer, Chen, Yuwen, Mazurowski, Maciej A.
Rok vydání:	2024
Předmět:	Electrical Engineering and Systems Science - Image and Video Processing Computer Science - Computer Vision and Pattern Recognition Computer Science - Software Engineering
Druh dokumentu:	Working Paper
Popis:	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. Comment: Future work: support for box and mask inputs for the video predictor of SAM 2
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2408.15224 Zobrazit plný text záznamu View this record from Arxiv