SAMCA: Segment Anything Model with Double Click Training and Shared Weight Adapter for Medical Ultrasound Image Segmentation

Segmentation of medical ultrasound images is crucial for clinical diagnosis. However, challenges such as low contrast and blurred boundaries make obtaining large-scale labeled data for model training difficult. The Segment Anything Model SAM , excelling at prompt-based segmentation in natural images, shows promise for ultrasound applications. In light of this, we propose SAMCA, a promptable medical ultrasound image segmentation model. SAMCA incorporates a shared weight adapter designed to efficiently transfer information between layers, allowing SAM to adapt to the complexities of medical ultrasound imaging. Additionally, we introduce a double click training strategy, where the first set of click prompts is used to provide guidance information for the initial target area, and the second set focuses on correcting local errors in the segmentation error-prone areas. A dynamic fusion mechanism ensures that the second set leverages the global context of the first set during refinement. Experimental comparisons with classic and recent segmentation networks demonstrate that SAMCA achieves state-of-the-art SOTA performance on the challenging TN3K and BUSI datasets, with DSC scores of 86.36 and 89.55 , respectively. Moreover, SAMCA is significantly more lightweight, requiring only 3 of parameter updates compared to SAM-Med2d. Our code will be publicly available at here.