<p>Glioma segmentation is crucial for brain tumor diagnosis, surgical planning, and prognosis assessment. Current glioma segmentation methods lack effective integration of 3D spatial and 2D multi-view features, especially in heterogeneous MRI data with varying tumor patterns across views and indistinct boundaries. We propose a boundary enhanced multi-view detail capture network, BMVDC-Net, using 3D-UNet architecture that effectively captures multi-view tumor distribution patterns and precise boundaries. First, we propose a multi-view feature representation module consisting of multi-scale axial, sagittal, coronal, and 3D convolutions to extract multidirectional tumor shape details. Second, we propose a boundary enhancement module that employs encoderdecoder parameter sharing, random boundary expansion, and attention fusion to make the network focus on tumor boundaries. Finally, we use post-processing to further refine the segmentation output. Experimental results on BraTS2019 and BraTS2023-GLI datasets demonstrate superior segmentation performance with 0.9 to 6.6 percent points improvement in mean Dice coefficient compared to state-of-the-art methods.</p>