Fig. 8
From: Fast cortical surface reconstruction from MRI using deep learning
Surface morphometries and anatomical parcellation from FastCSR showed high intra-subject test–retest reliability. To examine the reliability of our FastCSR method, we measured the instability of surface morphometries and anatomical parcellation in a dataset consisting of 30 participants with 10 repeated scans for each participant. A The instability of morphometrics and parcellations was estimated by the standard deviation across the 10 sessions in each vertex for each participant. The lower instability, indicated by red color, suggests higher test–retest reliability. The average instability map across 30 individuals showed similar distributions for both the FreeSurfer (the upper panel) and FastCSR (the lower panel) methods. However, the FastCSR show lower instability for cortical thickness, sulcal depth, and parcellation than FreeSurfer. B Histograms illustrate the distribution of measurements obtained from FastCSR (blue bars) and FreeSurfer (purple bars). FastCSR shows lower instability relative to FreeSurfer in measures of cortical thickness (two-sample Kolmogorov–Smirnov test, p = 1.130 × 10–7), sulcal depth (two-sample Kolmogorov–Smirnov test, p = 9.700 × 10–3), and anatomical parcellation (two-sample Kolmogorov–Smirnov test, p = 1.037 × 10–37)