The method combines deep learning–based brain parcellation with voxel-wise age prediction to capture structural patterns associated with aging. First, the brain is segmented into detailed anatomical regions using an ensemble of convolutional neural networks. Then, a dedicated model estimates age-related patterns across the brain, producing regional and global brain-age estimates. The output provides age estimation and volumetric measurements for each brain structure, enabling detailed exploration of brain aging patterns.