Self-supported Prototype Rectification for Few-shot Medical Image Segmentation

Few-shot semantic segmentation aims to quickly adapt to pixel-wise predictions for novel classes with only a few labeled images. Recent works rely on prototypical learning, where prototypes obtained from support images are applied to the segmentation of query images. However, there are inherent intra-class appearance differences between support images and query images, and the prototypes extracted from a small number of support images contain limited deep semantic information, which makes it difficult to accurately guide the segmentation of query images. To alleviate this problem, we propose a Self-Supported Prototype Rectification Network. Specifically, we introduce a Pseudo Mask Generation (PMG) module to generate a pseudo query mask by means of many-to-many prototype matching. We design a Prototype Rectification (PR) module with a learnable parameter λ to balance self-supported rectified prototype between support prototype obtained from support image and query prototype extracted from query features with pseudo query mask. Furthermore, we introduce a prototype-based multi-class segmentation approach mitigate the issue of confusion area prediction among different organs for query images in multi-organ segmentation scenario. Our method outperforms other SOTAs on two widely used datasets: CHAOST2 and MS-CMR.