Partition-A-Medical-Image: Extracting Multiple Representative Sub-regions for Few-shot Medical Image Segmentation

TL;DR

This paper introduces a novel method for few-shot medical image segmentation that extracts multiple sub-regions from support images, enabling more precise and adaptable segmentation by leveraging region-level representations and debiasing techniques.

Contribution

It proposes a regional prototypical learning framework with debiasing modules to improve few-shot medical image segmentation accuracy.

Findings

Consistent improvement over existing FSMIS methods on three datasets.

Effective suppression of regional representation disturbances.

Enhanced segmentation accuracy with multi-region analysis.

Abstract

Few-shot Medical Image Segmentation (FSMIS) is a more promising solution for medical image segmentation tasks where high-quality annotations are naturally scarce. However, current mainstream methods primarily focus on extracting holistic representations from support images with large intra-class variations in appearance and background, and encounter difficulties in adapting to query images. In this work, we present an approach to extract multiple representative sub-regions from a given support medical image, enabling fine-grained selection over the generated image regions. Specifically, the foreground of the support image is decomposed into distinct regions, which are subsequently used to derive region-level representations via a designed Regional Prototypical Learning (RPL) module. We then introduce a novel Prototypical Representation Debiasing (PRD) module based on a two-way elimination mechanism which suppresses the disturbance of regional representations by a self-support, Multi-direction Self-debiasing (MS) block, and a support-query, Interactive Debiasing (ID) block. Finally, an Assembled Prediction (AP) module is devised to balance and integrate predictions of multiple prototypical representations learned using stacked PRD modules. Results obtained through extensive experiments on three publicly accessible medical imaging datasets demonstrate consistent improvements over the leading FSMIS methods. The source code is available at https://github.com/YazhouZhu19/PAMI.