Perivascular Spaces Segmentation in Brain MRI Using Optimal 3D Filtering

Lucia Ballerini, Ruggiero Lovreglio, Maria Valdes Hernandez, Joel Ramirez, Bradley MacIntosh, Sandra Black, Joanna Wardlaw

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

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 fltering for extraction of PVS from MRI. We used ordered logit models and visual rating scales as alternative ground truth for Frangi flter 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
JournalScientific Reports
Publication statusPublished - 1 Feb 2018


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

Cite this