Multi-category Graph Reasoning for Multi-modal Brain Tumor Segmentation

Many multi-modal tumor segmentation methods have been proposed to localize diseased areas from the brain images, facilitating the intelligence of diagnosis. However, existing studies commonly ignore the relationship between multiple categories in brain tumor segmentation, leading to irrational tumor area distribution in the predictive results. To address this issue, this work proposes a Multi-category Region-guided Graph Reasoning Network, which models the dependency between multiple categories using a Transformer-based Multi-category Interaction Module (TMIM), thus enabling more accurate subregion localization of brain tumors. To improve the recognition of tumors’ blurred boundaries, a Region-guided Reasoning Module is also incorporated into the network, which captures semantic relationships between regions and contours via graph reasoning. In addition, we introduce a shared cross-attention encoder in the feature extraction stage to facilitate the comprehensive utilization of multi-modal information. Experimental results on the BraTS2019 and BraTS2020 datasets demonstrate that our method outperforms the current state-of-the-art methods.