DGP-HPCL: Dual-Prior-Guided Hypergraph Prototype Contrastive Learning for Brain Tumor Segmentation

Geometric supervision can improve segmentation outputs without specifying which intermediate features should participate in relational learning. We propose dual geometric prior-guided hypergraph prototype contrastive learning (DGP-HPCL) for multimodal brain tumor segmentation. A Gaussian center prior and an interior distance prior, both derived from training labels, jointly select decoder locations. Feature similarity then defines Soft-KNN hyperedges and their aggregated representations. Within each subject, the low- and high-center-response tails define two prototypes and the embeddings supervised by the contrastive objective. Each endpoint embedding is contrasted with both prototypes, favoring its own geometric group over the opposite group. Middle-ranked nodes remain available for aggregation without receiving binary prototype labels. Auxiliary prior prediction and cross-gated bidirectional consistency complement this relational objective during training. On BraTS 2019 five-fold validation, DGP-HPCL achieves 82.38% DSC, 73.54% surface Dice, and 6.91 HD95, the best means among the evaluated models. On the BraTS 2021 evaluation split, it achieves 88.87% DSC, 84.52% surface Dice, and 5.14 HD95. Direct cross-year evaluation and controlled design comparisons further support geometry-guided sampling and endpoint supervision. The framework regularizes a shared decoder during training and requires neither labels nor hypergraph construction at inference.