DA-Proto: Distribution-Aligned Prototype Learning for Cross-Domain Few-Shot Medical Image Segmentation

Few-Shot Medical Image Segmentation (FSMIS) aims to segment unseen categories with only a few annotated samples. However, domain shifts across imaging devices, protocols, and modalities can severely degrade target-domain performance, motivating Cross-Domain FSMIS (CD-FSMIS). Existing methods mainly exploit cross-domain stable cues in frequency, anatomical structure, or semantic channels, while largely overlooking the support-query foreground distributional relationship. Notably, we observe that support and query images of the same category exhibit consistent foreground distributions within each domain, and this relationship remains stable across domain shifts such as CT $\to$ MR. Based on this observation, we propose Distribution-Aligned Prototype Learning for Cross-Domain Few-Shot Medical Image Segmentation (DA-Proto), which extracts a stable foreground distribution prior from target-domain support samples and uses it to modulate domain-shifted class prototypes into distribution-aligned prototypes. Extensive experiments across multiple cross-domain settings demonstrate that DA-Proto significantly outperforms state-of-the-art methods.