HGMamba: A Heterogeneous Graph Mamba Framework for Interpretable Missing Modality Brain Tumor Segmentation

Multimodal MRI provides complementary information for brain tumor segmentation. In clinical practice, however, one or more modalities are often unavailable, which severely degrades models trained with complete modalities. Existing methods address this problem in limited ways. Some substitute missing modalities with zero-filled inputs, leading to biased feature representations. Others adopt multi-stage optimization with additional pretraining, increasing computational cost. Moreover, most methods overlook modality-specific characteristics and treat all modalities as equally informative for different tumor regions. To address these limitations, we propose HGMamba, an interpretable heterogeneous graph framework with Mamba-based interaction for missing-modality brain tumor segmentation. HGMamba models imaging modalities and hierarchical tumor regions, including the whole tumor, tumor core, and enhancing tumor, as heterogeneous graph nodes to capture modality-region dependencies. A data-driven affinity prior is introduced to quantify modality-region discriminability without additional learnable parameters and guide modality-to-region aggregation. A lightweight Mamba-based operator further enables efficient graph interaction, while a reliability gate and nested region interaction mechanism reduce the interference from missing modalities. Extensive experiments on BraTS 2018 and BraTS 2020 demonstrate that HGMamba achieves comparable Dice performance to the strongest competitor while substantially improving HD95. Furthermore, it achieves these improvements with an order of magnitude fewer parameters and over 2$\times$ faster inference.