Enlarged perivascular spaces in brain MRI: Automated quantification in four regions

• Published in: NeuroImage (Orlando, Fla.) Vol. 185; pp. 534 - 544
• Main Authors: Dubost, Florian, Yilmaz, Pinar, Adams, Hieab, Bortsova, Gerda, Ikram, M. Arfan, Niessen, Wiro, Vernooij, Meike, de Bruijne, Marleen
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 15.01.2019; Elsevier Limited
• Subjects: Aged; Aging; Agreements; Automation; Basal ganglia; Blood pressure; Brain - diagnostic imaging; Brain - pathology; Brain research; Cerebral Small Vessel Diseases - diagnostic imaging; Cerebral Small Vessel Diseases - pathology; Deep Learning; Dementia; Enlarged perivascular spaces; Epidemiology; Etiology; Female; Glymphatic System - diagnostic imaging; Glymphatic System - pathology; Hippocampus; Humans; Image Interpretation, Computer-Assisted - methods; Image processing; Machine learning; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Mesencephalon; Methods; Neural networks; Neuroimaging; Neuroimaging - methods; Perivascular spaces; Population studies; R&D; Research & development; Segmentation; Vascular diseases; Virchow-Robin spaces; Netherlands; Deep learning; Enlarged perivascular spaces; Perivascular spaces; Virchow-Robin spaces; Machine learning; Dementia
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2018.10.026

Enlarged perivascular spaces (PVS) are structural brain changes visible in MRI, are common in aging, and are considered a reflection of cerebral small vessel disease. As such, assessing the burden of PVS has promise as a brain imaging marker. Visual and manual scoring of PVS is a tedious and observer-dependent task. Automated methods would advance research into the etiology of PVS, could aid to assess what a “normal” burden is in aging, and could evaluate the potential of PVS as a biomarker of cerebral small vessel disease. In this work, we propose and evaluate an automated method to quantify PVS in the midbrain, hippocampi, basal ganglia and centrum semiovale. We also compare associations between (earlier established) determinants of PVS and visual PVS scores versus the automated PVS scores, to verify whether automated PVS scores could replace visual scoring of PVS in epidemiological and clinical studies. Our approach is a deep learning algorithm based on convolutional neural network regression, and is contingent on successful brain structure segmentation. In our work we used FreeSurfer segmentations. We trained and validated our method on T2-contrast MR images acquired from 2115 subjects participating in a population-based study. These scans were visually scored by an expert rater, who counted the number of PVS in each brain region. Agreement between visual and automated scores was found to be excellent for all four regions, with intraclass correlation coefficients (ICCs) between 0.75 and 0.88. These values were higher than the inter-observer agreement of visual scoring (ICCs between 0.62 and 0.80). Scan-rescan reproducibility was high (ICCs between 0.82 and 0.93). The association between 20 determinants of PVS, including aging, and the automated scores were similar to those between the same 20 determinants of PVS and visual scores. We conclude that this method may replace visual scoring and facilitate large epidemiological and clinical studies of PVS.