Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-23357
Publication type: Article in scientific journal
Type of review: Peer review (publication)
Title: Automatic quantification of white matter hyperintensities on T2-weighted fluid attenuated inversion recovery magnetic resonance imaging
Authors: Igwe, Kay C.
Lao, Patrick J.
Vorburger, Robert S.
Banerjee, Arit
Rivera, Andres
Chesebro, Anthony
Laing, Krystal
Manly, Jennifer J.
Brickman, Adam M.
et. al: No
DOI: 10.1016/j.mri.2021.10.007
10.21256/zhaw-23357
Published in: Magnetic Resonance Imaging
Volume(Issue): 85
Page(s): 71
Pages to: 79
Issue Date: Oct-2021
Publisher / Ed. Institution: Elsevier
ISSN: 0730-725X
1873-5894
Language: English
Subjects: Automated segmentation; Half Gaussian mixture model; Mixture model; Small vessel cerebrovascular disease; White matter hyperintensity
Subject (DDC): 616.8: Neurology, diseases of nervous system
Abstract: White matter hyperintensities (WMH) are areas of increased signal visualized on T2-weighted fluid attenuated inversion recovery (FLAIR) brain magnetic resonance imaging (MRI) sequences. They are typically attributed to small vessel cerebrovascular disease in the context of aging. Among older adults, WMH are associated with risk of cognitive decline and dementia, stroke, and various other health outcomes. There has been increasing interest in incorporating quantitative WMH measurement as outcomes in clinical trials, observational research, and clinical settings. Here, we present a novel, fully automated, unsupervised detection algorithm for WMH segmentation and quantification. The algorithm uses a robust preprocessing pipeline, including brain extraction and a sample-specific mask that incorporates spatial information for automatic false positive reduction, and a half Gaussian mixture model (HGMM). The method was evaluated in 24 participants with varying degrees of WMH (4.9-78.6 cm3) from a community-based study of aging and dementia with dice coefficient, sensitivity, specificity, correlation, and bias relative to the ground truth manual segmentation approach performed by two expert raters. Results were compared with those derived from commonly used available WMH segmentation packages, including SPM lesion probability algorithm (LPA), SPM lesion growing algorithm (LGA), and Brain Intensity AbNormality Classification Algorithm (BIANCA). The HGMM algorithm derived WMH values that had a dice score of 0.87, sensitivity of 0.89, and specificity of 0.99 compared to ground truth. White matter hyperintensity volumes derived with HGMM were strongly correlated with ground truth values (r = 0.97, p = 3.9e-16), with no observable bias (-1.1 [-2.6, 0.44], p-value = 0.16). Our novel algorithm uniquely uses a robust preprocessing pipeline and a half-Gaussian mixture model to segment WMH with high agreement with ground truth for large scale studies of brain aging.
Further description: https://pubmed.ncbi.nlm.nih.gov/34662699/
URI: https://digitalcollection.zhaw.ch/handle/11475/23357
Fulltext version: Accepted version
License (according to publishing contract): CC BY-NC-ND 4.0: Attribution - Non commercial - No derivatives 4.0 International
Restricted until: 2022-10-16
Departement: Life Sciences and Facility Management
Organisational Unit: Institute of Computational Life Sciences (ICLS)
Appears in collections:Publikationen Life Sciences und Facility Management

Files in This Item:
File Description SizeFormat 
2021_Igwe-etal_Quantification-white-matter-hyperintensities.pdf
  Until 2022-10-16
Accepted Version809.9 kBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.