Mask Matching Network for Self-supervised Few-shot Medical Image Segmentation

Existing few-shot segmentation methods have achieved remarkable progress in medical image segmentation. However, many existing methods yield incomplete and discontinuous boundary predictions. In contrast, the Segment Anything Model (SAM) consistently produces clear, continuous, and comprehensive segmentation boundaries. Building on this observation, we propose a new two-step network called Mask Matching Network (MMNet) to introduce extra knowledge learned by SAM in natural images for few-shot medical image segmentation. Firstly, Q-Net has been utilized to locate some Regions of Interest (RoI) as prompts for SAM, allowing for the automatic generation of masks without relying on manual prompts. Secondly, we propose a novel Mask Matching Module (MMM), which considers both feature similarity and volume similarity as guidance to collaboratively mine the final segmentation from proposal masks. MMNet achieves state-of-the-art performance with remarkable improvements on two widely used datasets, abdominal MR (ABD) and cardiac MR (CMR), under two different settings.