An Automatic Brain Cortex Segmentation Technique Based on Dynamic Recalibration and Region Awareness

To address the limitations in the accuracy of current cerebral cortex structure segmentation methods, this study proposes an automatic segmentation network based on dynamic recalibration and region awareness. The network is an improved version of the classic U-shaped architecture, incorporating a Dynamic Recalibration Block (DRB) and a Region-Aware Block (RAB). The DRB enhances important feature channels by extracting global feature information across channels, computing the significance weights via a two-layer fully connected network, and applying these weights to the original feature maps for dynamic feature reweighting. Meanwhile, the RAB integrates spatial positional information and captures both global and local context across multiple dimensions. It recalibrates features using dimension-specific weights, enabling region-aware feature association and complementing the DRB’s function. Together, these components enable efficient and accurate segmentation of brain structures. The proposed DRA-Net model effectively overcomes the accuracy–efficiency trade-off in cortical segmentation through multi-scale feature fusion, dual attention mechanisms, and deep feature extraction strategies. Experimental results demonstrate that DRA-Net achieves an average Dice score of 91.35% across multiple datasets, outperforming segmentation atlases based on methods such as U-Net, QuickNAT, and FastSurfer.