ePrints

Cross-domain Few-shot Medical Image Segmentation via Dynamic Semantic Matching

Lookup NU author(s): Dr Shidong WangORCiD

Downloads

Full text for this publication is not currently held within this repository. Alternative links are provided below where available.

Abstract

© 1992-2012 IEEE.Cross-domain few-shot medical image segmentation (CDFSMIS) presents the fundamental challenge of segmenting novel anatomical or tissue structures on unfamiliar medical imaging domains with limited annotated data. In this paper, we conduct an in-depth investigation of CDFSMIS and identify two critical observations: (a) the conventional matching mechanisms from existing few-shot models are particularly vulnerable to discrepancies in local characteristics between different domains and (b) the semantic representations learned from source domains often lack robustness when generalizing to unfamiliar target domains. Motivated by these insights, we propose a novel Dynamic Semantic Matching (DSM) framework that addresses these challenges through a three-component approach. First, we design a support-query feature re-weighting (SFR) mechanism that leverages multilevel hidden features to suppress domain-specific contents. Second, we introduce a dynamic semantic information selection (DSIS) strategy that adaptively identifies and combines domain-robust channels to construct generalizable representations. Third, we develop a dual-perspective semantic center calculation method to address the inherent texture imbalance in medical images. Extensive experiments on four unfamiliar target domains (MS-CMR, PI-PMR, Chest-X-Ray and ISIC2018) demonstrate that our approach significantly outperforms state-of-the-art few-shot segmentation and cross-domain few-shot segmentation models, validating the effectiveness of DSM in simultaneously addressing domain generalization and semantic matching challenges in medical image segmentation.

Publication metadata

Author(s): Zhu Y, Wang S, Zhou T, Li Z, Zhang H, Shao L

Publication type: Article

Publication status: Published

Journal: IEEE Transactions on Image Processing

Year: 2025

Volume: 34

Pages: 6669-6682

Online publication date: 13/10/2025

Acceptance date: 29/09/2025

ISSN (print): 1057-7149

ISSN (electronic): 1941-0042

Publisher: Institute of Electrical and Electronics Engineers Inc.

URL: https://doi.org/10.1109/TIP.2025.3618396

DOI: 10.1109/TIP.2025.3618396

Altmetrics

Share