Support-Query Prototype Fusion Network for Few-shot Medical Image Segmentation

TL;DR

This paper introduces SQPFNet, a novel few-shot medical image segmentation model that fuses support and query prototypes to improve segmentation accuracy, achieving state-of-the-art results on public datasets.

Contribution

The paper proposes a support-query prototype fusion approach that leverages both support and query information for enhanced few-shot segmentation performance.

Findings

Achieves state-of-the-art results on SABS and CMR datasets.

Effectively constructs high-quality fused prototypes for segmentation.

Outperforms existing prototype-based methods in few-shot medical image segmentation.

Abstract

In recent years, deep learning based on Convolutional Neural Networks (CNNs) has achieved remarkable success in many applications. However, their heavy reliance on extensive labeled data and limited generalization ability to unseen classes pose challenges to their suitability for medical image processing tasks. Few-shot learning, which utilizes a small amount of labeled data to generalize to unseen classes, has emerged as a critical research area, attracting substantial attention. Currently, most studies employ a prototype-based approach, in which prototypical networks are used to construct prototypes from the support set, guiding the processing of the query set to obtain the final results. While effective, this approach heavily relies on the support set while neglecting the query set, resulting in notable disparities within the model classes. To mitigate this drawback, we propose a novel Support-Query Prototype Fusion Network (SQPFNet). SQPFNet initially generates several support prototypes for the foreground areas of the support images, thus producing a coarse segmentation mask. Subsequently, a query prototype is constructed based on the coarse segmentation mask, additionally exploiting pattern information in the query set. Thus, SQPFNet constructs high-quality support-query fused prototypes, upon which the query image is segmented to obtain the final refined query mask. Evaluation results on two public datasets, SABS and CMR, show that SQPFNet achieves state-of-the-art performance.