Automated segmentation of deep brain structures from Inversion-Recovery MRI

Methods for the automated segmentation of brain structures are a major subject of medical research. The small structures of the deep brain have received scant attention, notably for lack of manual delineations by medical experts. In this study, we assessed an automated segmentation of a novel clinical dataset containing White Matter Attenuated Inversion-Recovery (WAIR) MRI images and five manually segmented structures (substantia nigra (SN), subthalamic nucleus (STN), red nucleus (RN), mammillary body (MB) and mammillothalamic fascicle (MT-fa)) in 53 patients with severe Parkinson’s disease. T1 and DTI images were additionally used. We also assessed the reorientation of DTI diffusion vectors with reference to the ACPC line. A state-of-the-art nnU-Net method was trained and tested on subsets of 38 and 15 image datasets respectively. We used Dice similarity coefficient (DSC), 95% Hausdorff distance (95HD), and volumetric similarity (VS) as metrics to evaluate network efficiency in reproducing manual contouring. Random-effects models statistically compared values according to structures, accounting for between- and within-participant variability. Results show that WAIR significantly outperformed T1 for DSC (0.739 ± 0.073), 95HD (1.739 ± 0.398), and VS (0.892 ± 0.044). The DSC values for automated segmentation of MB, RN, SN, STN, and MT-fa decreased in that order, in line with the increasing complexity observed in manual segmentation. Based on training results, the reorientation of DTI vectors improved the automated segmentation.