MAEM-ResUNet: Accurate Glioma Segmentation in Brain MRI via Symmetric Multi-Directional Mamba and Dual-Attention Modules

Gliomas are among the most common and aggressive malignant brain tumors. Their irregular morphology and fuzzy boundaries pose substantial challenges for automatic segmentation in MRI. Accurate delineation of tumor subregions is crucial for treatment planning and outcome assessment. This study proposes MAEM-ResUNet, an extension of the ResUNet architecture that integrates three key modules: a multi-scale adaptive attention module for joint channel–spatial feature selection, a symmetric multi-directional Mamba block for long-range context modeling, and an adaptive edge attention module for boundary refinement. Experimental results on the BraTS2020 and BraTS2021 datasets demonstrate that MAEM-ResUNet outperforms mainstream methods. On BraTS2020, it achieves an average Dice Similarity Coefficient of 91.19% and an average Hausdorff Distance (HD) of 5.27 mm; on BraTS2021, the average Dice coefficient is 89.67% and the average HD is 5.87 mm, both showing improvements compared to other mainstream models. Meanwhile, ablation experiments confirm the synergistic effect of the three modules, which significantly enhances the accuracy of glioma segmentation and the precision of boundary localization.