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Perivascular Spaces (PVS) are a recently recognised feature of Small VesselDisease (SVD), also indicating neuroinflammation, and are an important part ofthe brain's circulation and glymphatic drainage system. Quantitative analysisof PVS on Magnetic Resonance Images (MRI) is important for understanding theirrelationship with neurological diseases. In this work, we propose asegmentation technique based on the 3D Frangi filtering for extraction of PVSfrom MRI. Based on prior knowledge from neuroradiological ratings of PVS, weused ordered logit models to optimise Frangi filter parameters in response tothe variability in the scanner's parameters and study protocols. We optimizedand validated our proposed models on two independent cohorts, a dementia sample(N=20) and patients who previously had mild to moderate stroke (N=48). Resultsdemonstrate the robustness and generalisability of our segmentation method.Segmentation-based PVS burden estimates correlated with neuroradiologicalassessments (Spearman's $\rho$ = 0.74, p $<$ 0.001), suggesting the greatpotential of our proposed method

Perivascular Spaces Segmentation in Brain MRI Using Optimal 3D Filtering