MBUNeXt: Multibranch Encoder Aggregation Network Based on Layer-Fusion Strategy for Multimodal Brain Tumor Segmentation

Abstract

Multimodal brain tumor segmentation (BraTS), integrated with surgical robots and navigation systems, enables accurate surgical interventions while maximizing the preservation of surrounding healthy brain tissue. However, multimodal brain scans suffer from large interclass differences in brain tumor subregions and information redundancy, leading to inadequate fusion of multimodal information and significantly affecting the accuracy of BraTS. To address the above problems, we propose a multibranch encoder aggregation (MEA) network based on a layer-fusion strategy called multibranch UNeXt (MBUNeXt). The network comprises three well-designed modules: the multimodal feature attention (MFA) module, the MEA module, and the large-kernel convolution skip (LCS)-connection module. These modules work together to achieve precise segmentation of brain tumors. Specifically, the MFA module preserves the intermodality similarity structure through attention mechanisms and Gaussian modulation functions, thereby filtering redundant information. Then, the MEA module exploits the correlations among multiple modalities to effectively integrate multimodal hybrid feature representation and optimize multimodal information fusion. In addition, the LCS module constructs multiple groups of depthwise separable convolutions with large kernel, which can guide the network to attend to features at different scales, thereby addressing the issue of significant interclass differences in brain tumor subregions. The experimental results on the large-scale public datasets, BraTS2019 and BraTS2021, which consist of approximately 5000 3-D brain scans, demonstrate that our proposed method has achieved SOTA performance, with average Dice scores of 85.84% and 91.11%, respectively. It also performs well on the BraTS-Africa2024 dataset with low imaging quality, confirming its robustness. The code is available at https://github.com/liuqinghao2018/MBUNeXt.