Segment Anything in Medical Images and Videos: Benchmark and Deployment
<|reference_start|>Segment Anything in Medical Images and Videos: Benchmark and Deployment: 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}.<|reference_end|>
@article{ma2024segment,
    title={Segment Anything in Medical Images and Videos: Benchmark and Deployment},
    author={Jun Ma, Sumin Kim, Feifei Li, Mohammed Baharoon, Reza Asakereh,
  Hongwei Lyu, and Bo Wang},
    journal={arXiv preprint arXiv:2408.03322},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.03322},
    primaryClass={eess.IV cs.CV}
}