Few-Shot Medical Image Segmentation with High-Fidelity Prototypes

TL;DR

This paper introduces DSPNet, a novel method for few-shot medical image segmentation that constructs high-fidelity prototypes by modeling multi-modal structures and channel-specific information, significantly improving performance on challenging benchmarks.

Contribution

The paper proposes DSPNet, a new prototype network that enhances few-shot medical image segmentation by capturing detailed semantics and background structures more effectively.

Findings

DSPNet outperforms previous methods on three medical image benchmarks.

High-fidelity prototypes improve segmentation accuracy in complex medical images.

Model effectively captures both foreground and background details.

Abstract

Few-shot Semantic Segmentation (FSS) aims to adapt a pretrained model to new classes with as few as a single labelled training sample per class. Despite the prototype based approaches have achieved substantial success, existing models are limited to the imaging scenarios with considerably distinct objects and not highly complex background, e.g., natural images. This makes such models suboptimal for medical imaging with both conditions invalid. To address this problem, we propose a novel Detail Self-refined Prototype Network (DSPNet) to constructing high-fidelity prototypes representing the object foreground and the background more comprehensively. Specifically, to construct global semantics while maintaining the captured detail semantics, we learn the foreground prototypes by modelling the multi-modal structures with clustering and then fusing each in a channel-wise manner. Considering that the background often has no apparent semantic relation in the spatial dimensions, we integrate channel-specific structural information under sparse channel-aware regulation. Extensive experiments on three challenging medical image benchmarks show the superiority of DSPNet over previous state-of-the-art methods.