VesselBoost: A Python Toolbox for Small Blood Vessel Segmentation in Human Magnetic Resonance Angiography Data

Abstract

Magnetic resonance angiography (MRA) performed at ultra-high magnetic field provides a unique opportunity to study the arteries of the living human brain at the mesoscopic level. From this, we can gain new insights into the brain’s blood supply and vascular disease affecting small vessels. However, for quantitative characterization and precise representation of human angioarchitecture to, for example, inform blood-flow simulations, detailed segmentations of the smallest vessels are required. Given the success of deep learning-based methods in many segmentation tasks, we explore their application to high-resolution MRA data and address the difficulty of obtaining large data sets of correctly and comprehensively labelled data. We introduce VesselBoost, a vessel segmentation toolbox, which utilizes deep learning and imperfect training labels for accurate vasculature segmentation. To enhance the segmentation models’ robustness and accuracy, VesselBoost employs an innovative data augmentation technique, which captures the resemblance of vascular structures across scales by zooming in or out on input image patches—virtually creating diverse scale vascular data. This approach enables detailed vascular segmentation and ensures the model’s ability to generalize across various scales of vascular structures.