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

Abstract

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 domain 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 PED datasets, demonstrate that SAPNet consistently outperforms state-of-the-art UDA and FSS methods, exhibiting strong generalization and robustness.