IMH-Net: Importance-aware Mamba and Cross-modal Hypergraph Modeling for Precise PET/CT Tumor Segmentation

• IMH-Net boosts segmentation accuracy via salient-first modeling and hypergraph fusion • IA-Mamba prioritizes salient regions and preserves fine-grained local details • CSCEM boosts channel spatial complementarity and suppresses crossmodal noise • CHB capture high-order skip connection dependencies to recover details in decoding • Experiments show IMH-Net beats prior methods and stays SOTAcompetitive Precise multimodal tumor segmentation is essential for radiotherapy target contouring, surgical planning, and therapeutic efficacy evaluation. PET provides metabolic activity information, whereas CT offers detailed anatomical structures; their complementarity improves segmentation reliability in complex cases. However, existing sequence-modeling schemes are susceptible to order bias induced by a fixed scanning order, and cross-modal fusion and skip-connection interactions often remain at low-order, coarse-grained levels, making it difficult to jointly achieve salient-region–prioritized modeling, noise suppression, and high-order semantic coupling. To address this, we propose IMH-Net, an automatic multimodal tumor segmentation network based on importance-aware Mamba and hypergraph modeling. The proposed network includes three core components: (1) importance-aware Mamba (IA-Mamba), which estimates patch importance in the encoder stage and dynamically reshuffles the scan order to model salient regions first. (2) The Cross-modal Spatial Channel Enhancement Module (CSCEM) performs cross-modal collaborative enhancement in both channel and spatial dimensions at the bottleneck, emphasizing complementary semantics while suppressing redundant conflicts. (3) The Cross-modal Hypergraph Bridge (CHB) constructs intra- and inter-modality hyperedges at skip connections and leverages hypergraph convolution and hypergraph attention to enable stable high-order interactions and feature coupling. Comprehensive experiments on the public STS, Hecktor 2022, and ECPC datasets validate both the effectiveness of the proposed modules and their complementary synergy. IMH-Net achieves Dice scores of 81.82%, 80.86%, and 91.40% on STS, Hecktor 2022, and ECPC datasets, respectively, outperforming state-of-the-art (SOTA) multimodal segmentation methods in overall performance.