Standardization of hippocampus volumetry using automated brain structure volumetry tool for an initial Alzheimer’s disease imaging biomarker

Abstract

Background One significant barrier to incorporate Alzheimer’s disease (AD) imaging biomarkers into diagnostic criteria is the lack of standardized methods for biomarker quantification. The European Alzheimer’s Disease Consortium-Alzheimer’s Disease Neuroimaging Initiative (EADC-ADNI) Harmonization Protocol project provides the most authoritative guideline for hippocampal definition and has produced a manually segmented reference dataset for validation of automated methods. Purpose To validate automated hippocampal volumetry using AccuBrain™, against the EADC-ADNI dataset, and assess its diagnostic performance for differentiating AD and normal aging in an independent cohort. Material and Methods The EADC-ADNI reference dataset comprise of manually segmented hippocampal labels from 135 volumetric T1-weighted scans from various scanners. Dice similarity coefficient (DSC), intraclass correlation coefficient (ICC), and Pearson’s r were obtained for AccuBrain™ and FreeSurfer. The magnetic resonance imaging (MRI) of a separate cohort of 299 individuals (150 normal controls, 149 with AD) were obtained from the ADNI database and processed with AccuBrain™ to assess its diagnostic accuracy. Area under the curve (AUC) for total hippocampal volumes (HV) and hippocampal fraction (HF) were determined. Results Compared with EADC-ADNI dataset ground truths, AccuBrain™ had a mean DSC of 0.89/0.89/0.89, ICC of 0.94/0.96/0.95, and r of 0.95/0.96/0.95 for right/left/total HV. AccuBrain™ HV and HF had AUC of 0.76 and 0.80, respectively. Thresholds of ≤ 5.71 mL and ≤ 0.38% afforded 80% sensitivity for AD detection. Conclusion AccuBrain™ provides accurate automated hippocampus segmentation in accordance with the EADC-ADNI standard, with great potential value in assisting clinical diagnosis of AD.