Automatic segmentation of lenticulostriate arteries from 7T contrast-enhanced MR angiography in patients with cerebral small vessel disease

Motivation: 7T TOF MRA detects the lenticulostriate arteries (LSA), which perfuse important subcortical structures and are implicated in the pathogenesis of cerebral small vessel disease (SVD). Goal(s): This study aimed to automatically segment LSAs from 7T TOF MRA for SVD patients, to facilitate studies of the arterial pathology of SVD. Approach: We applied a state-of-the-art deep learning model “DS6” and a classical multi-scale Frangi filter pipeline to 7T contrast-enhanced TOF MRA scans from 8 SVD patients for LSA segmentation. Results: Both approaches showed comparable and satisfactory performance with mean test dice score=0.74. DS6 was more robust but less sensitive to lower-intensity arteries. Impact: We present an automatic pipeline for 3D segmentation of the lenticulstriate arteries (LSAs) from 7T TOF MRA. This will enable clinical studies to characterise LSA morphology in cerebral small vessel disease which will open new avenues to understand its pathophysiology.