Evidence Harmonization Network for Tumor Segmentation with Missing Modalities

Accurate segmentation of brain tumors is crucial for effective diagnosis and treatment planning. Magnetic Resonance Imaging (MRI) with multiple modalities provides comprehensive biological information for tumor diagnosis. However, the absence of certain modalities degrades segmentation performance. To address this challenge, we propose an Evidence Harmonization Network (EHNet), which is designed for brain tumor segmentation in scenarios with missing modalities. Firstly, we use subjective logic to model uncertainty, replacing missing modality evidence with averaged evidence from available modality. Secondly, we introduce the Evidence Harmonization Module, which captures the distinction and consensus among modalities and calculates a harmonization factor. It enables the network to dynamically extract effective features in missing modalities scenarios and fully utilizes the strengths of each available modalities for final segmentation. Finally, the Dynamic Fusion Module adjusts the evidence with the harmonization factor and fuses it to produce the final segmentation results. We validate our method on the widely used Brats2018 dataset, demonstrating its superior performance compared to existing methods in missing modalities scenarios. Our approach not only achieves accurate segmentation results but also provides reliable uncertainty estimation.