Elastic Flow-guided Few-shot Medical Image Segmentation with SAM-Refined

Few-shot medical image segmentation focuses on generalizing to unseen categories with limited annotations. However, medical images are characterized by widespread non-rigid deformations and significant intra-class differences, leading to a dilemma of support-query foreground ideal matching. This constitutes a critical factor limiting performance of many existing approaches. To overcome this issue, we propose an Elastic Flow-guided few-shot medical image segmentation with SAM-Refined network (EFG-SAM). First, to alleviate deformation gap between support and query foreground features, we design an Elastic Flow-field Deformation Module (EFDM). Building upon deformed support features, we propose a Deformation-aware Generation Module (DGM) to obtain elastic prototypes containing semantic, details, and background information. Subsequently, leveraging semantic elastic prototypes to suppress background noise in query features, we design an Elastic Localization Module (ELM). Then, we propose an Elastic Aggregation Module (EAM) to achieve efficient segmentation of query features under the guidance of these elastic prototypes. Finally, during the inference stage, SAM is seamlessly integrated into EFG as a training-free enhancement component to improve final segmentation results. Extensive experiments on three datasets show that our method outperforms state-of-the-art approaches by 2.45% on average and by 8.30% in cross-domain settings, demonstrating its effectiveness in bridging the deformation gap.