Jill Abrigo1, Lin Shi1,2,3, Yishan Luo3, Qianyun Chen1, Winnie Chiu Wing Chu1, Vincent Chung Tong Mok2,4. 1. 1 Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong SAR, China. 2. 2 Chow Yuk Ho Technology Centre for Innovative Medicine, Therese Pei Fong Chow Research Center for Prevention of Dementia, Lui Che Woo Institute of Innovative Medicine, Gerald Choa Neuroscience Center, The Chinese University of Hong Kong, Hong Kong SAR, China. 3. 3 BrainNow Medical Technology Limited, Hong Kong Science and Technology Park, Hong Kong SAR, China. 4. 4 Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China.
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.
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.
Authors: Zhiyu Cao; Yingren Mai; Wenli Fang; Ming Lei; Yishan Luo; Lei Zhao; Wang Liao; Qun Yu; Jiaxin Xu; Yuting Ruan; Songhua Xiao; Vincent C T Mok; Lin Shi; Jun Liu Journal: Front Hum Neurosci Date: 2022-06-14 Impact factor: 3.473
Authors: Bonnie Yin Ka Lam; Kam Tat Leung; Brian Yiu; Lei Zhao; J Matthijs Biesbroek; Lisa Au; Yumi Tang; Kai Wang; Yuhua Fan; Jian-Hui Fu; Qun Xu; Haiqing Song; Xiaolin Tian; Winnie Chiu Wing Chu; Jill Abrigo; Lin Shi; Ho Ko; Alexander Lau; Marco Duering; Adrian Wong; Vincent Chung Tong Mok Journal: Alzheimers Dement (Amst) Date: 2019-10-25