EPPNet: Edge Prototype Purification with Auxiliary Supervision for Few-Shot Medical Image Segmentation

Medical image segmentation plays a pivotal role in computer-aided diagnosis. However, the scarcity of annotated data severely hinders the deployment of deep learning models. Few-shot learning (FSL) is designed to achieve rapid adaptation to unseen classes using limited labeled samples, among which prototype-based methods have emerged as a dominant paradigm. Nevertheless, existing approaches often rely on single or coarse multi-prototype representations, failing to capture complex morphological variations and local details in medical images. Furthermore, boundary feature contamination arising from convolutional receptive fields severely degrades segmentation accuracy. To address these challenges, this paper proposes an Auxiliary Supervision-guided Edge Prototype Purification Network (EPPNet) for few-shot medical image segmentation. Specifically, the network introduces a novel Prototype Purification Module (PPM). By evaluating the semantic consistency between edge and main prototypes, the PPM selectively integrates high-confidence edge prototypes via a learnable adaptive threshold, aiming to substantially alleviate boundary feature contamination. Simultaneously, an Adaptive Prototype Generation (APG) module and an Adaptive Weight Decoding (AWD) mechanism are designed to dynamically extract semantic-aware prototypes for foreground, background, and edge regions, while assigning optimal fusion weights. Furthermore, a parallel U-Net auxiliary branch is constructed to enhance the generalization capability and spatial context modeling of the feature encoder through fully supervised dense pixel prediction. Extensive experiments on three public datasets (CHAOS-MRI, Synapse-CT, and CMRSeg) demonstrate that the proposed method outperforms existing state-of-the-art techniques across multiple organ segmentation tasks. Detailed analyses and validation further confirm significant improvements in prototype representation and boundary information utilization.