Precise perivascular space segmentation on magnetic resonance imaging from Human Connectome Project-Aging.

Abstract

Perivascular spaces (PVS) are cerebrospinal fluid-filled tunnels around brain blood vessels, crucial for the functions of the glymphatic system. Changes in PVS have been linked to vascular diseases and aging, necessitating accurate segmentation for further study. PVS segmentation poses challenges due to their small size, varying MRI appearances, and the scarcity of annotated data. We present a finely segmented PVS dataset from T2-weighted MRI scans, sourced from the Human Connectome Project Aging (HCP-Aging), encompassing 200 subjects aged 30 to 100. Our approach utilizes a combination of unsupervised and deep learning techniques with manual corrections to ensure high accuracy. This dataset aims to facilitate research on PVS dynamics across different ages and to explore their link to cognitive decline. It also supports the development of advanced image segmentation algorithms, contributing to improved medical imaging automation and the early detection of neurodegenerative diseases.