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

## TL;DR

This paper introduces a robust 3D Frangi filter-based segmentation method for extracting perivascular spaces from brain MRI, validated across different cohorts, and correlates well with expert assessments, aiding neurological disease research.

## Contribution

The study presents a novel optimized 3D filtering technique for PVS segmentation that adapts to scanner variability and demonstrates strong validation results.

## Key findings

- Segmentation correlates with neuroradiological ratings (Spearman's ρ=0.74)
- Method is robust across different patient cohorts
- Optimized parameters improve segmentation accuracy

## Abstract

Perivascular Spaces (PVS) are a recently recognised feature of Small Vessel Disease (SVD), also indicating neuroinflammation, 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. Based on prior knowledge from neuroradiological ratings of PVS, we used ordered logit models to optimise Frangi filter parameters in response to the variability in the scanner's parameters and study protocols. 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 with neuroradiological assessments (Spearman's $\rho$ = 0.74, p $<$ 0.001), suggesting the great potential of our proposed method