Structure-Aware Attention Prototype Network for Cross-Modality Few-Shot Brain Tumor Segmentation

Multi-modal magnetic resonance imaging (MRI) plays a crucial role in brain tumor diagnosis. However, the substantial physiological sensitivity discrepancies across imaging modalities create a severe domain gap that challenges current cross-modality segmentation methods. Many unsupervised do main adaptation (UDA) approaches reduce this gap through image style translation or distribution alignment, which have shown promising results but may struggle to preserve modality specific anatomical and pathological information. In contrast, few-shot segmentation (FSS) provides a promising alternative paradigm with strong cross-domain generalization capability, avoiding explicit domain alignment. Therefore, in this work, we propose a novel Structure-aware Attention Prototype Network (SAPNet) for cross-modality few-shot brain tumor segmentation, which fully exploits task-agnostic, multi-scale features from the large vision model. Specifically, our SAPNet consists of three main components. First, we introduce a masked support feature reconstruction (MSFR) module to encourage the network to understand anatomically meaningful structures. Second, a patch-level attention-to-prototype alignment (APA) module is proposed to adaptively balance the aggressive and conservative segmentation tendencies by cross-attention (CA) and prototype based learning. Third, a lightweight multi-scale decoder with the contrastive embedding space is employed to enhance fine grained pixel-level prediction. Extensive experiments on three public benchmarks, BraTS 2020, VS-SEG, and BraTS 2023 PEDdatasets, demonstrate that SAPNet consistently outperforms state-of-the-art UDA and FSS methods, exhibiting strong gener alization and robustness.