Automated Methods for Vessel Segmentation in X-ray Coronary Angiography and Geometric Modeling of Coronary Angiographic Image Sequences: A Survey

Heart disease is the leading cause of death worldwide, responsible for 16% of the world’s total deaths. Coronary artery disease is the most common type of heart disease and is caused by plaque buildup in the wall of the arteries. X-ray coronary angiography is the gold standard for assessing coronary artery disease via estimation of the percentage of narrowing, also known as stenosis. However, errors in physicians’ interpretation of stenosis severity could lead to overuse and underuse of revascularization. Automated techniques can help clinicians in their decision-making. In this paper, we review state-of-the-art techniques developed for coronary artery segmentation, vessel modeling, and stenosis detection. The methods are categorized using criteria such as segmentation methods, evaluation metrics, and validation approaches.