DPEA-Net: a clinically-oriented lightweight 3D CNN for glioma segmentation in multiparametric MRI

Objective: Accurate segmentation of glioma subregions, whole tumor (WT), tumor core (TC), and enhancing tumor (ET), from multiparametric MRI is essential for radiotherapy planning and longitudinal assessment. Methods: We propose DPEA-Net, a lightweight 3D architecture that addresses these challenges through two novel components. First, the Dynamic Hierarchically Decoupled Convolution (DHDC) unit reduces parameters by 99% compared to 3D U-Net while enabling adaptive multi-scale feature extraction to handle tumor heterogeneity. Second, the Cross-Dimensional Region-Specific Enhancement Attention (CDRSEA) module explicitly models 3D spatial relationships to refine ambiguous tumor boundaries. Results: On the BraTS 2019 and 2020 validation sets, DPEA-Net achieves mean Dice scores of 90.43%/89.96% (WT), 85.56%/86.52% (TC), and 81.89%/80.31% (ET) with a computational footprint of only 17.48 Giga Floating-point Operations (GFLOPs), enabling sub-2-second inference on standard clinical hardware. A 1.5-fold TC weighting strategy further enhances segmentation of the clinically critical tumor core. Conclusion: DPEA-Net provides an accurate and computationally efficient tool for automated glioma subregion delineation, supporting practical integration into neuro-oncological workflows.