Automated Machine Learning-Based Brain Artery Segmentation, Anatomical Prior Labeling, and Feature Extraction on MR Angiography

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Cerebrovascular disease is a major cause of death and disability globally. Time-of-flight magnetic resonance angiography (TOF-MRA) is a common noninvasive technique to evaluate vascular abnormalities. Accurate segmentation and feature extraction of cerebral vessels from TOF-MRA data is crucial for diagnosis and treatment of cerebrovascular diseases. However, manual annotation of vessels is a time-consuming task even for experts, and an automatic segmentation method can speed-up the task significantly. Although several machine learning-based post-processing approaches exist, these are limited to vessel segmentation only and require large manually segmented training datasets. Further, image resolution plays an important role to segment the vessels as well as extracting features (e.g., diameters, number of branches, tortuosity) accurately. Aim 1: To develop an automated anatomical prior based machine learning framework for brain artery segmentation with minimal training MR high resolution data followed by feature extractions on low resolution MRA from an existing database. Aim 2: To assess the relationship between targeted vessel features and neuroimaging biomarkers of cerebral small vessel disease (CSVD). Aim 3: To assess the relationship between targeted vessel features and cognitive performance in HIV CSVD cohort. To achieve this goal, we will implement our recently proposed automated vessel segmentation and feature extraction pipeline “BayesTract” in combination with a super-resolution approach. We will validate it using in-vivo MRI scans. We will then examine the vascular features and their associations with CSVD markers and cognitive performance in an existing dataset (101 HIV+ and 102 HIV- controls with CSVD). We hypothesize that this approach will provide more accurate and time-efficient measures compared to existing approaches. We also hypothesize that vascular features and their associations with CSVD markers and cognition will be significantly different between those with and without CSVD. This study helps advance the state-of-the-art in brain vessel segmentation and feature extractions from non- invasive TOF-MRA, which could hasten the translation of vessel related biomarkers into the clinical setting. This will be essential in evaluating promising interventions and ultimately, lead to ameliorating patient outcome and quality of life.