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 (Medical image analysis, 2025)
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. [Display omitted] •We present Med-SA, a simple yet powerful extension of SAM for medical applications.•Our method is one of the first adaption methods to transfer SAM to medical domain.•We propose SD-Trans and HyP-Adpt by considering the real challenges in medical domain.•Experiments on 17 tasks with various modalities establish Med-SA’s superiority.