Modal Feature Supplementation Enhances Brain Tumor Segmentation

Abstract

For patients with brain tumors, effectively utilizing the complementary information between multimodal medical images is crucial for accurate lesion segmentation. However, effectively utilizing the complementary features across different modalities remains a challenging task. To address these challenges, we propose a modal feature supplement network (MFSNet), which extracts modality features simultaneously using both a main and an auxiliary network. During this process, the auxiliary network supplements the modality features of the main network, enabling accurate brain tumor segmentation. We also design a modal feature enhancement module (MFEM), a cross-layer feature fusion module (CFFM), and an edge feature supplement module (EFSM). MFEM enhances the network performance by fusing the modality features from the main and auxiliary networks. CFFM supplements additional contextual information by fusing features from adjacent encoding layers at different scales, which are then passed into the corresponding decoding layers. This aids the network in preserving more details during upsampling. EFSM improves network performance by using deformable convolution to extract challenging boundary lesion features, which are then used to supplement the final output of the decoding layer. We evaluated MFSNet on the BraTS2018 and BraTS2021 datasets. The Dice scores for the whole tumor, tumor core, and enhancing tumor regions were 90.86%, 90.59%, 84.72%, and 92.28%, 92.47%, 86.07%, respectively. This validates the accuracy of MFSNet in brain tumor segmentation, demonstrating its superiority over other networks of similar type.