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

arXiv: https://arxiv.org/abs/1704.07699 · HTML: https://mlsanity.com/paper/1704.07699/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Apr 2017.

## Built on by (most cited first, 5 of 5)

- [Diffusion based multi-domain neuroimaging harmonization method with preservation of anatomical details](https://mlsanity.com/paper/2409.00807/) (2024)
- [Computer-Aided Extraction of Select MRI Markers of Cerebral Small Vessel Disease: A Systematic Review](https://mlsanity.com/paper/2204.01411/) (2022)
- [Deep Learning Based Detection of Enlarged Perivascular Spaces on Brain MRI](https://mlsanity.com/paper/2209.13727/) (2022)
- [Standardized Evaluation of Automatic Methods for Perivascular Spaces Segmentation in MRI -- MICCAI 2024 Challenge Results](https://mlsanity.com/paper/2512.18197/) (2025)
- [Multi-task learning for the automatic grading of enlarged perivascular space burden using MRI](https://mlsanity.com/paper/2609.37387/) (2026)

## Abstract (arXiv)

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 ρ = 0.74, p < 0.001), suggesting the great potential of our proposed method