Inter‐method reliability between automatic region of interest analytic application with multi‐atlas segmentation and FreeSurfer. Issue 5 (8th June 2020) - Utsumi, Tomohiro; Kodaka, Fumitoshi; Maikusa, Norihide; Yamazaki, Ryuichi; Shigeta, Masahiro
Abstract : Aim: Alzheimer's disease (AD) is a progressive neurodegenerative disease characterized by the aggregation of amyloid‐β and phosphorylated tau proteins. Magnetic resonance imaging (MRI) is a useful means of detecting hippocampal atrophy. However, instead of visual inspection, objective and time‐saving tools for automated region of interest (ROI) analysis are needed. Advances in MRI segmentation techniques have enabled a multi‐atlas approach with fewer errors than a conventional single‐atlas approach. To support the clinical application of multi‐atlas segmentation, an automated ROI analytic application consisting of multi‐atlas segmentation with joint label fusion and corrective learning was developed: T‐Proto. In the present study, we evaluated the inter‐method reliability between T‐Proto and a reference ROI analytic software, FreeSurfer. Methods: This was a database study. MRI data from 30 patients with AD were selected, and the inter‐method reliability was assessed in terms of the intra‐class correlation coefficient (ICC). A post‐hoc comparison according to the severity of AD was also performed. Results: Almost all the regional volumes estimated with T‐Proto were smaller than those estimated with FreeSurfer. The regional ICC values between the two methods showed moderate to excellent reliability. A post‐hoc comparison revealed a similar t‐ value and effect size between both methods for the hippocampus. Conclusion: In the present study, we showed that automated regional analysis using T‐Proto was reliable in the hippocampus in terms of ICC, compared with FreeSurfer.