Enhanced Region-Aware Fusion Network: A Multimodal Deep Learning Framework for 3D Brain Tumor Segmentation in MRI Volumes

Effectively segmenting brain tumors from multimodal 3D MRI scans presents a formidable challenge due to the heterogeneous nature of tumor structures and the inherent modality imbalance. This study presents the Enhanced Region-Aware Fusion Network, an end-to-end architecture that integrates spatial-probabilistic reasoning with adaptive modality fusion for volumetric medical image segmentation. The Probability Map Estimator is the fundamental module of this study. It generates region confidence maps, guiding an Enhanced Region-Aware Fusion Module to study dynamic attention weights across various MRI modalities. Then the fused representations are further polished by a shared decoder to enable precise tumor description. Enhanced Region-Aware Fusion Network simultaneously minimizes both Binary Cross-Entropy and Dice losses, thereby enhancing sensitivity at tumor boundaries and effectively addressing label imbalance. We evaluated our model on the BRA TS2020 dataset. Results show that Enhanced Region-aware Fusion Network surpasses the conventional multimodal fusion approaches while maintaining spatial consistency within tumor regions. Enhanced Region-Aware Fusion Network highlights its promise for clinical MRI tumor diagnosis.