An anatomical knowledge-based MRI deep learning pipeline for white matter hyperintensity quantification associated with cognitive impairment

Highlights•An anatomical knowledge-based MRI deep learning pipeline was developed to simultaneously segment and locate white matter hyperintensities. •This pipeline significantly improved segmentation performance, especially in cohorts with mild lesion burden. •Different white matter hyperintensitie...

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Bibliographic Details
Published in:Computerized medical imaging and graphics 2021-04, Vol.89, p.101873-101873, Article 101873
Main Authors: Liang, Li, Zhou, Pengzheng, Lu, Wanxin, Guo, Xutao, Ye, Chenfei, Lv, Haiyan, Wang, Tong, Ma, Ting
Format: Article
Language:English
Subjects:
Aging
Algorithms
Alzheimer's disease
Anatomical knowledge
Brain
Cognition
Cognition & reasoning
Cognitive ability
Cognitive impairment
Configurations
Corpus callosum
Decision making
Deep learning
Geriatrics
Internal Medicine
Knowledge
Lesions
Localization
Machine learning
Magnetic resonance imaging
Metric space
Neurodegenerative diseases
Older people
Other
Pipelines
Segmentation
Segments
Spatial data
Substantia alba
White matter hyperintensities
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Summary:Highlights•An anatomical knowledge-based MRI deep learning pipeline was developed to simultaneously segment and locate white matter hyperintensities. •This pipeline significantly improved segmentation performance, especially in cohorts with mild lesion burden. •Different white matter hyperintensities patterns were associated with normal aging and cognitive impairments.
ISSN:0895-6111
1879-0771
DOI:10.1016/j.compmedimag.2021.101873