PSP: Harnessing Position and Shape Priors for Cross-Domain Few-Shot Medical Image Segmentation

2026-06-27

AI Summary

This work addresses the performance degradation in cross-modal few-shot medical image segmentation caused by texture discrepancies across imaging domains. To mitigate this issue, the authors propose a novel approach that explicitly decouples semantic structure from domain-specific appearance. By leveraging the consistent organ location and geometric shape across modalities—a structural prior modeled for the first time in this context—the method introduces three key components: Position Coordinate Embedding (PCE), Shape Prototype Modulation (SPM), and Hybrid Prototype Prediction (HPP). These modules collectively enhance generalization by effectively exploiting structural consistency. Experimental results on two public medical imaging datasets demonstrate that the proposed method significantly outperforms current state-of-the-art approaches, achieving substantial improvements in cross-domain segmentation performance.

Abstract

Few-Shot Medical Image Segmentation (FSMIS) offers a powerful solution to data scarcity but struggles to generalize across different imaging modalities. This performance collapse stems primarily from the drastic texture discrepancies between domains, which mislead models trained on source-specific intensity distributions. While existing methods attempt to align frequency or local texture features, they often fail to decouple semantic structure from domain-specific appearance. To address this, we identify a critical invariance: despite distinct imaging physics, the position and geometric shape of organs remain robustly consistent across modalities. Therefore, we propose a novel framework that harnesses Position and Shape Priors (PSP) for cross-domain FSMIS. Specifically, PSP first introduces a Position Coordinate Embedding (PCE) module to inject relative spatial coordinates for rapid organ localization. Subsequently, a Shape Prototype Modulation (SPM) module constructs domain-invariant structural prototypes via explicit shape priors, effectively filtering out texture noise. Furthermore, the Hybrid-Prototype Prediction (HPP) module adaptively calibrates the support prototype to the query feature distribution, mitigating feature misalignment. Extensive experiments on two public medical imaging datasets demonstrate that PSP significantly outperforms state-of-the-art methods.

Problem

• Few-Shot Medical Image Segmentation
• Cross-Domain
• Domain Generalization
• Texture Discrepancy
• Medical Imaging

Innovation

• Position and Shape Priors
• Cross-Domain Few-Shot Segmentation
• Shape Prototype Modulation
• Position Coordinate Embedding
• Domain-Invariant Representation