Automated Surface-Based Segmentation of Deep Grey Matter Brain Regions Based Solely on Diffusion Tensor Images

A surface-based deep grey matter segmentation algorithm is proposed that works directly on diffusion images and maps of the brain acquired at a 1.5 mm isotropic resolution. The method was applied to twenty participants spanning a large age range (6-90 years) resulting in accurate segmentations of the thalamus, caudate, putamen and globus pallidus. Fractional anisotropy and mean diffusivity showed unique non-linear trajectories across the lifespan. The proposed method avoids the need of problematic coregistration to other scans (anatomical T1) and will accelerate the analysis of microstructural changes of deep grey matter regions with age (or disease) in large populations.