Iterative Self-Guided Prototype Enhancement Network for Few-Shot Medical Image Segmentation
Year: 2026
Type: article
Abstract: Few-shot medical image segmentation aims to delineate previously unseen anatomical structures using only a few labeled samples. Prototype-based methods have emerged as the dominant paradigm due to their strong generalization ability. However, most existing methods follow a query-agnostic prototype generation paradigm, overlooking intra-class variation. To overcome this limitation, we propose theIteRativeSelf-guidedPrototypeEnhancement Network (IR-SPENet), a query-centric framework that progressively refines support prototypes to better adapt to diverse query appearances. Specifically, we design a Hybrid Prototype Generation (HPG) module that constructs a global prototype to represent holistic semantics, together with an adaptive number of local prototypes to encode fine-grained details, enabling multi-granularity modeling of support-query discrepancies. Nevertheless, due to large appearance variations, not all local support prototypes are equally informative for a given query. To selectively emphasize relevant prototypes, we introduce a Query-guided Support Prototype Refinement (Q-SPR) module, which leverages optimal transport to re-weight local support prototypes according to query-specific information. By iteratively applying Q-SPR, IR-SPENet progressively enhances prototype quality and robustness against intra-class variation. Extensive experiments on three public medical image segmentation benchmarks demonstrate that IR-SPENet consistently outperforms existing methods, achieving leading performance.
Source: IEEE Transactions on Multimedia
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,645
OpenAlex ID: W7204190724