RobustEMD: Domain robust matching for cross-domain few-shot medical image segmentation.

Abstract

Few-shot medical image segmentation (FSMIS) aims to perform the limited annotated data learning in the medical image analysis scope. Despite the progress has been achieved, current FSMIS models are all trained and deployed on the same data domain, as is not consistent with the clinical reality that medical imaging data is always across different data domains (e.g. imaging modalities, institutions and equipment sequences). In this paper, we introduce Cross-domain Few-shot Medical Image Segmentation (CD-FSMIS) and propose a RobustEMD matching mechanism based on Earth Mover's Distance (EMD) to enhance cross-domain generalization. Our approach includes three key components: (1) a channel-wise feature decomposition strategy that uniformly divides support and query features into local nodes, (2) a texture structure aware weights generation method that restrains domain-relevant nodes through Sobel-based gradient calculation, and (3) a boundary-aware Hausdorff distance measurement for transportation cost calculation. Extensive experiments across three scenarios (cross-modal, cross-sequence and cross-institution) show that our method significantly outperforms existing approaches. And ablation studies further confirm that each component of our RobustEMD mechanism contributes to the enhanced performance. The experimental outcomes highlight strong generalization capabilities of our model in real-world heterogeneous medical imaging environments. Code is available at https://github.com/YazhouZhu19/RobustEMD.