A comprehensive framework for automated segmentation of perivascular spaces in brain MRI with the nnU-Net

Abstract

Background: Enlargement of perivascular spaces (PVS) is common in cerebral small vessel disease, Alzheimer’s disease, and Parkinson’s disease, reflecting impaired clearance pathways. While MRI provides a means to quantify perivascular spaces, manual annotation remains time-consuming and labour-intensive. Thus, there is a need for accurate automated MRI-based PVS segmentation methods. Aim: To optimise the nnU-Net, a deep-learning framework, for PVS segmentation. Methods: 30 T1-weighted (T1w) MRI images acquired on three different scanners were used. PVS in the white matter (WM) and basal ganglia (BG) were manually labelled via a sparse annotation strategy and used to optimise the nnU-Net for ..