Introduction: Arterial brain vessel assessment is crucial for the diagnostic process
in patients with cerebrovascular disease. Non-invasive neuroimaging techniques, such
as time-of-flight (TOF) magnetic resonance angiography (MRA) imaging are applied in
the clinical routine to depict arteries. They are, however, only visually assessed. Fully
automated vessel segmentation integrated into the clinical routine could facilitate the
time-critical diagnosis of vessel abnormalities and might facilitate the identification of
valuable biomarkers for cerebrovascular events. In the present work, we developed and
validated a new deep learning model for vessel segmentation, coined BRAVE-NET, on a
large aggregated dataset of patients with cerebrovascular diseases.
Methods: BRAVE-NET is a multiscale 3-D convolutional neural network (CNN) model
developed on a dataset of 264 patients from three different studies enrolling patients
with cerebrovascular diseases. A context path, dually capturing high- and low-resolution
volumes, and deep supervision were implemented. The BRAVE-NET model was
compared to a baseline Unet model and variants with only context paths and deep
supervision, respectively. The models were developed and validated using high-quality
manual labels as ground truth. Next to precision and recall, the performance was
assessed quantitatively by Dice coefficient (DSC); average Hausdorff distance (AVD);
95-percentile Hausdorff distance (95HD); and via visual qualitative rating.
Results: The BRAVE-NET performance surpassed the other models for arterial brain
vessel segmentation with a DSC = 0.931, AVD = 0.165, and 95HD = 29.153. The
BRAVE-NET model was also the most resistant toward false labelings as revealed by the
visual analysis. The performance improvement is primarily attributed to the integration
Hilbert et al. Fully-Automated Arterial Brain Vessel Segmentation
of the multiscaling context path into the 3-D Unet and to a lesser extent to the deep
supervision architectural component.
Discussion: We present a new state-of-the-art of arterial brain vessel segmentation
tailored to cerebrovascular pathology. We provide an extensive experimental validation
of the model using a large aggregated dataset encompassing a large variability of
cerebrovascular disease and an external set of healthy volunteers. The framework
provides the technological foundation for improving the clinical workflow and can serve
as a biomarker extraction tool in cerebrovascular diseases