Inter-method reliability between automatic region of interest analytic application with multi-atlas segmentation and
Tomohiro Utsumi1, Fumitoshi Kodaka1, Norihide Maikusa1
1Department of Psychiatry, The Jikei University School of Medicine, Tokyo, Japan.
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.
