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Medical SAM adapter: Adapting segment anything model for medical image segmentation
Wu, Junde Wang, Ziyue Hong, Mingxuan Ji, Wei Fu, Huazhu Xu, Yanwu Xu, Min Jin, Yueming
Wu, Junde
Wang, Ziyue
Hong, Mingxuan
Ji, Wei
Fu, Huazhu
Xu, Yanwu
Xu, Min
Jin, Yueming
Supervisor
Department
Computer Vision
Embargo End Date
Type
Journal article
Date
2025
License
Language
English
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Research Projects
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Journal Issue
Abstract
The Segment Anything Model (SAM) has recently gained popularity in the field of image segmentation due to its impressive capabilities in various segmentation tasks and its prompt-based interface. However, recent studies and individual experiments have shown that SAM underperforms in medical image segmentation due to the lack of medical-specific knowledge. This raises the question of how to enhance SAM's segmentation capability for medical images. We propose the Medical SAM Adapter (Med-SA), which is one of the first methods to integrate SAM into medical image segmentation. Med-SA uses a light yet effective adaptation technique instead of fine-tuning the SAM model, incorporating domain-specific medical knowledge into the segmentation model. We also propose Space-Depth Transpose (SD-Trans) to adapt 2D SAM to 3D medical images and Hyper-Prompting Adapter (HyP-Adpt) to achieve prompt-conditioned adaptation. Comprehensive evaluation experiments on 17 medical image segmentation tasks across various modalities demonstrate the superior performance of Med-SA while updating only 2% of the SAM parameters (13M). Our code is released at https://github.com/KidsWithTokens/Medical-SAM-Adapter.
Citation
J. Wu et al., “Medical SAM adapter: Adapting segment anything model for medical image segmentation,” Med Image Anal, vol. 102, p. 103547, May 2025, doi: 10.1016/J.MEDIA.2025.103547.
Source
Medical Image Analysis
Conference
Keywords
Efficient Learning, Fine-Tuning, Medical Image Segmentation
Subjects
Source
Publisher
Elsevier