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Published in: BMC Medical Imaging 1/2022

Open Access 01-12-2022 | Alzheimer's Disease | Research

Automated brain volumetric measures with AccuBrain: version comparison in accuracy, reproducibility and application for diagnosis

Authors: Lei Zhao, Yishan Luo, Vincent Mok, Lin Shi

Published in: BMC Medical Imaging | Issue 1/2022

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Abstract

Background

Automated brain volumetry has been widely used to assess brain volumetric changes that may indicate clinical states and progression. Among the tools that implement automated brain volumetry, AccuBrain has been validated for its accuracy, reliability and clinical applications for the older version (IV1.2). Here, we aim to investigate the performance of an updated version (IV2.0) of AccuBrain for future use from several aspects.

Methods

Public datasets with 3D T1-weighted scans were included for version comparisons, each with Alzheimer’s disease (AD) patients and normal control (NC) subjects that were matched in age and gender. For the comparisons of the brain volumetric measures quantified from the same scans, we investigated the difference of hippocampal segmentation accuracy (using Dice similarity coefficient [DSC] as the major measurement). As AccuBrain generates a composite index (AD resemblance atrophy index, AD-RAI) that indicates similarity with AD-like brain atrophy pattern, we also compared the two versions for the diagnostic accuracy of AD versus NC with AD-RAI. Also, we examined the intra-scanner reproducibility of the two versions for the scans acquired with short-intervals using intraclass correlation coefficient.

Results

AccuBrain IV2.0 presented significantly higher accuracy of hippocampal segmentation (DSC: 0.91 vs. 0.89, p < 0.001) and diagnostic accuracy of AD (AUC: 0.977 vs. 0.921, p < 0.001) than IV1.2. The results of intra-scanner reproducibility did not favor one version over the other.

Conclusions

AccuBrain IV2.0 presented better segmentation accuracy and diagnostic accuracy of AD, and similar intra-scanner reproducibility compared with IV1.2. Both versions should be feasible for use due to the small magnitude of differences.
Literature
1.
go back to reference Giorgio A, De Stefano N. Clinical use of brain volumetry. J Magn Reson Imaging. 2013;37(1):1–14.CrossRef Giorgio A, De Stefano N. Clinical use of brain volumetry. J Magn Reson Imaging. 2013;37(1):1–14.CrossRef
2.
go back to reference Abrigo J, Shi L, Luo Y, Chen Q, Chu WCW, Mok VCT, et al. Standardization of hippocampus volumetry using automated brain structure volumetry tool for an initial Alzheimer’s disease imaging biomarker. Acta Radiol. 2019;60(6):769–76.CrossRef Abrigo J, Shi L, Luo Y, Chen Q, Chu WCW, Mok VCT, et al. Standardization of hippocampus volumetry using automated brain structure volumetry tool for an initial Alzheimer’s disease imaging biomarker. Acta Radiol. 2019;60(6):769–76.CrossRef
3.
go back to reference Liu S, Hou B, Zhang Y, Lin T, Fan X, You H, et al. Inter-scanner reproducibility of brain volumetry: influence of automated brain segmentation software. BMC Neurosci. 2020;21(1):35.CrossRef Liu S, Hou B, Zhang Y, Lin T, Fan X, You H, et al. Inter-scanner reproducibility of brain volumetry: influence of automated brain segmentation software. BMC Neurosci. 2020;21(1):35.CrossRef
4.
go back to reference Wang C, Zhao L, Luo Y, Liu J, Miao P, Wei S, et al. Structural covariance in subcortical stroke patients measured by automated MRI-based volumetry. Neuroimage Clin. 2019;22:101682.CrossRef Wang C, Zhao L, Luo Y, Liu J, Miao P, Wei S, et al. Structural covariance in subcortical stroke patients measured by automated MRI-based volumetry. Neuroimage Clin. 2019;22:101682.CrossRef
5.
go back to reference Liu C, Zhao L, Yang S, Luo Y, Zhu W, Zhu W, et al. Structural changes in the lobar regions of brain in cerebral small-vessel disease patients with and without cognitive impairment: an MRI-based study with automated brain volumetry. Eur J Radiol. 2020;126:108967.CrossRef Liu C, Zhao L, Yang S, Luo Y, Zhu W, Zhu W, et al. Structural changes in the lobar regions of brain in cerebral small-vessel disease patients with and without cognitive impairment: an MRI-based study with automated brain volumetry. Eur J Radiol. 2020;126:108967.CrossRef
6.
go back to reference Zhang Y, Dou W, Zuo Z, You H, Lv Y, Hou B, et al. Brain volume and perfusion asymmetry in temporal lobe epilepsy with and without hippocampal sclerosis. Neurol Res. 2021;43(4):299–306.CrossRef Zhang Y, Dou W, Zuo Z, You H, Lv Y, Hou B, et al. Brain volume and perfusion asymmetry in temporal lobe epilepsy with and without hippocampal sclerosis. Neurol Res. 2021;43(4):299–306.CrossRef
7.
go back to reference Hou B, Gao L, Shi L, Luo Y, Guo X, Young GS, et al. Reversibility of impaired brain structures after transsphenoidal surgery in Cushing’s disease: a longitudinal study based on an artificial intelligence-assisted tool. J Neurosurg. 2020;1(aop):1–10.CrossRef Hou B, Gao L, Shi L, Luo Y, Guo X, Young GS, et al. Reversibility of impaired brain structures after transsphenoidal surgery in Cushing’s disease: a longitudinal study based on an artificial intelligence-assisted tool. J Neurosurg. 2020;1(aop):1–10.CrossRef
8.
go back to reference Zhao L, Zhang X, Luo Y, Hu J, Liang C, Wang L, et al. Automated detection of hippocampal sclerosis: comparison of a composite MRI-based index with conventional MRI measures. Epilepsy Res. 2021;174:106638.CrossRef Zhao L, Zhang X, Luo Y, Hu J, Liang C, Wang L, et al. Automated detection of hippocampal sclerosis: comparison of a composite MRI-based index with conventional MRI measures. Epilepsy Res. 2021;174:106638.CrossRef
9.
go back to reference Zheng Y, Guo H, Zhang L, Wu J, Li Q, Lv F. Machine learning-based framework for differential diagnosis between vascular dementia and Alzheimer’s disease using structural MRI features. Front Neurol. 2019;10:1097.CrossRef Zheng Y, Guo H, Zhang L, Wu J, Li Q, Lv F. Machine learning-based framework for differential diagnosis between vascular dementia and Alzheimer’s disease using structural MRI features. Front Neurol. 2019;10:1097.CrossRef
10.
go back to reference Yu Q, Mai Y, Ruan Y, Luo Y, Zhao L, Fang W, et al. An MRI-based strategy for differentiation of frontotemporal dementia and Alzheimer’s disease. Alzheimers Res Ther. 2021;13(1):23.CrossRef Yu Q, Mai Y, Ruan Y, Luo Y, Zhao L, Fang W, et al. An MRI-based strategy for differentiation of frontotemporal dementia and Alzheimer’s disease. Alzheimers Res Ther. 2021;13(1):23.CrossRef
11.
go back to reference Zhao L, Luo Y, Lew D, Liu W, Au L, Mok V, et al. Risk estimation before progression to mild cognitive impairment and Alzheimer’s disease: an AD resemblance atrophy index. Aging (Albany NY). 2019;11(16):6217–36.CrossRef Zhao L, Luo Y, Lew D, Liu W, Au L, Mok V, et al. Risk estimation before progression to mild cognitive impairment and Alzheimer’s disease: an AD resemblance atrophy index. Aging (Albany NY). 2019;11(16):6217–36.CrossRef
12.
go back to reference Mai Y, Yu Q, Zhu F, Luo Y, Liao W, Zhao L, et al. AD resemblance atrophy index as a diagnostic biomarker for Alzheimer’s disease: a retrospective clinical and biological validation. J Alzheimers Dis. 2021;79(3):1023–32.CrossRef Mai Y, Yu Q, Zhu F, Luo Y, Liao W, Zhao L, et al. AD resemblance atrophy index as a diagnostic biomarker for Alzheimer’s disease: a retrospective clinical and biological validation. J Alzheimers Dis. 2021;79(3):1023–32.CrossRef
13.
go back to reference Liu W, Au LWC, Abrigo J, Luo Y, Wong A, Lam BYK, et al. MRI-based Alzheimer’s disease-resemblance atrophy index in the detection of preclinical and prodromal Alzheimer’s disease. Aging (Albany NY). 2021;13(10):13496–514.CrossRef Liu W, Au LWC, Abrigo J, Luo Y, Wong A, Lam BYK, et al. MRI-based Alzheimer’s disease-resemblance atrophy index in the detection of preclinical and prodromal Alzheimer’s disease. Aging (Albany NY). 2021;13(10):13496–514.CrossRef
14.
go back to reference Jack CR Jr., Bernstein MA, Fox NC, Thompson P, Alexander G, Harvey D, et al. The Alzheimer’s Disease neuroimaging initiative (ADNI): MRI methods. J Magn Reson Imaging. 2008;27(4):685–91.CrossRef Jack CR Jr., Bernstein MA, Fox NC, Thompson P, Alexander G, Harvey D, et al. The Alzheimer’s Disease neuroimaging initiative (ADNI): MRI methods. J Magn Reson Imaging. 2008;27(4):685–91.CrossRef
15.
go back to reference Frisoni GB, Jack CR Jr., Bocchetta M, Bauer C, Frederiksen KS, Liu Y, et al. The EADC-ADNI harmonized protocol for manual hippocampal segmentation on magnetic resonance: evidence of validity. Alzheimers Dement. 2015;11(2):111–25.CrossRef Frisoni GB, Jack CR Jr., Bocchetta M, Bauer C, Frederiksen KS, Liu Y, et al. The EADC-ADNI harmonized protocol for manual hippocampal segmentation on magnetic resonance: evidence of validity. Alzheimers Dement. 2015;11(2):111–25.CrossRef
16.
go back to reference Boccardi M, Bocchetta M, Morency FC, Collins DL, Nishikawa M, Ganzola R, et al. Training labels for hippocampal segmentation based on the EADC-ADNI harmonized hippocampal protocol. Alzheimers Dement. 2015;11(2):175–83.CrossRef Boccardi M, Bocchetta M, Morency FC, Collins DL, Nishikawa M, Ganzola R, et al. Training labels for hippocampal segmentation based on the EADC-ADNI harmonized hippocampal protocol. Alzheimers Dement. 2015;11(2):175–83.CrossRef
17.
go back to reference Malone IB, Cash D, Ridgway GR, MacManus DG, Ourselin S, Fox NC, et al. MIRIAD–public release of a multiple time point Alzheimer’s MR imaging dataset. Neuroimage. 2013;70:33–6.CrossRef Malone IB, Cash D, Ridgway GR, MacManus DG, Ourselin S, Fox NC, et al. MIRIAD–public release of a multiple time point Alzheimer’s MR imaging dataset. Neuroimage. 2013;70:33–6.CrossRef
18.
go back to reference Luo Y, inventorAutomatic quantitation method for regional brain atrophy degree. China patent CN107103612A. 2017. Luo Y, inventorAutomatic quantitation method for regional brain atrophy degree. China patent CN107103612A. 2017.
19.
go back to reference DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44(3):837–45.CrossRef DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44(3):837–45.CrossRef
20.
go back to reference Worker A, Dima D, Combes A, Crum WR, Streffer J, Einstein S, et al. Test-retest reliability and longitudinal analysis of automated hippocampal subregion volumes in healthy ageing and Alzheimer’s disease populations. Hum Brain Mapp. 2018;39(4):1743–54.CrossRef Worker A, Dima D, Combes A, Crum WR, Streffer J, Einstein S, et al. Test-retest reliability and longitudinal analysis of automated hippocampal subregion volumes in healthy ageing and Alzheimer’s disease populations. Hum Brain Mapp. 2018;39(4):1743–54.CrossRef
Metadata
Title
Automated brain volumetric measures with AccuBrain: version comparison in accuracy, reproducibility and application for diagnosis
Authors
Lei Zhao
Yishan Luo
Vincent Mok
Lin Shi
Publication date
01-12-2022
Publisher
BioMed Central
Published in
BMC Medical Imaging / Issue 1/2022
Electronic ISSN: 1471-2342
DOI
https://doi.org/10.1186/s12880-022-00841-2

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