Towards robust brain tumor segmentation under modality incompleteness: A contribution-optimized edge-enhanced network

Abstract

Multimodal medical image segmentation plays a crucial role in disease diagnosis, as different MRI modalities provide complementary structural and lesion information. However, in clinical practice, the absence of certain modalities often leads to a significant decline in segmentation performance, limiting the application of multimodal methods. To address this issue, we propose a multimodal segmentation model called MECS-Net, which combines modality contribution optimization, edge enhancement, and efficient feature fusion. Based on four MRI modalities (Flair, T1ce, T1, T2), we further introduce edge features as auxiliary modalities to enhance the perception of critical structural boundaries. The model incorporates a modality contribution measurement mechanism to quantify the actual predictive value of each modality at the sample level and performs resampling training on low-contribution modalities to mitigate performance degradation caused by modality missing. The feature fusion module combines multi-head cross-attention and state space modeling (Mamba), where the former enhances fine-grained interactions between modalities and the latter models cross-modal global dependencies, synergistically improving semantic alignment and fusion effects. Extensive experiments on the BraTS 2020 dataset demonstrate that MECS-Net achieves outstanding performance under both complete and incomplete modality conditions. The Dice coefficients for WT (whole tumor area) and TC (tumor core area) reach 91.8% and 86.4%, respectively, under complete modality conditions, and average 86.7% and 79.1%, respectively, under incomplete modality conditions.