Recent advances in segmentation foundation models have enabled accurate and efficient segmentation across a wide range of natural images and videos, but their utility to medical data remains unclear. In this work, we first present a comprehensive benchmarking of the Segment Anything Model 2 (SAM2) across 11 medical image modalities and videos and point out its strengths and weaknesses by comparing it to SAM1 and MedSAM. Then, we develop a transfer learning pipeline and demonstrate SAM2 can be quickly adapted to medical domain by fine-tuning. Furthermore, we implement SAM2 as a 3D slicer plugin and Gradio API for efficient 3D image and video segmentation. The code has been made publicly available at \url{https://github.com/bowang-lab/MedSAM}.
@article{arxiv.2408.03322,
title = {Segment Anything in Medical Images and Videos: Benchmark and Deployment},
author = {Jun Ma and Sumin Kim and Feifei Li and Mohammed Baharoon and Reza Asakereh and Hongwei Lyu and Bo Wang},
journal= {arXiv preprint arXiv:2408.03322},
year = {2024}
}