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Sci Rep. 2018 Feb 1;8(1):2132. doi: 10.1038/s41598-018-19781-5.

Perivascular Spaces Segmentation in Brain MRI Using Optimal 3D Filtering.

Author information

1
Department of Neuroimaging Sciences, Centre for Clinical Brain Sciences, Centre for Cognitive Ageing and Cognitive Epidemiology and UK Dementia Research Institute Edinburgh Dementia Research Centre, University of Edinburgh, Edinburgh, UK. lucia.ballerini@ed.ac.uk.
2
Department of Civil and Environmental Engineering, University of Auckland, Auckland, New Zealand.
3
Department of Neuroimaging Sciences, Centre for Clinical Brain Sciences, Centre for Cognitive Ageing and Cognitive Epidemiology and UK Dementia Research Institute Edinburgh Dementia Research Centre, University of Edinburgh, Edinburgh, UK.
4
Hurvitz Brain Sciences Program, LC Campbell Cognitive Neurology Research Unit, Heart and Stroke Foundation Canadian Partnership for Stroke Recovery, Sunnybrook Research Institute and the University of Toronto, Toronto, Ontario, Canada.

Abstract

Perivascular Spaces (PVS) are a feature of Small Vessel Disease (SVD), and are an important part of the brain's circulation and glymphatic drainage system. Quantitative analysis of PVS on Magnetic Resonance Images (MRI) is important for understanding their relationship with neurological diseases. In this work, we propose a segmentation technique based on the 3D Frangi filtering for extraction of PVS from MRI. We used ordered logit models and visual rating scales as alternative ground truth for Frangi filter parameter optimization and evaluation. We optimized and validated our proposed models on two independent cohorts, a dementia sample (N = 20) and patients who previously had mild to moderate stroke (N = 48). Results demonstrate the robustness and generalisability of our segmentation method. Segmentation-based PVS burden estimates correlated well with neuroradiological assessments (Spearman's ρ = 0.74, p < 0.001), supporting the potential of our proposed method.

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