Perivascular Spaces Segmentation in Brain MRI Using Optimal 3D Filtering

Lucia Ballerini (Lead / Corresponding author), Ruggiero Lovreglio, Maria Del C. Valdés Hernández, Joel Ramirez, Bradley J. MacIntosh, Sandra E. Black, Joanna M. Wardlaw

Research output: Contribution to journalArticlepeer-review

88 Citations (Scopus)
169 Downloads (Pure)


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.

Original languageEnglish
Article number2132
Pages (from-to)1-11
Number of pages11
JournalScientific Reports
Issue number1
Early online date1 Feb 2018
Publication statusPublished - 2018

ASJC Scopus subject areas

  • General


Dive into the research topics of 'Perivascular Spaces Segmentation in Brain MRI Using Optimal 3D Filtering'. Together they form a unique fingerprint.

Cite this