Coronary artery disease is the most common type of heart disease, which influences 110 million people's health and causes 8.9 million deaths in 2015. Physicians can visualize the lesion in coronary arteries by cardiac angiography (X-ray image) during diagnosis and treatment of coronary artery disease. The pathological findings in cardiac angiography are reported per segment or per artery of the coronary artery tree, therefore, it requires to annotate the name of each segment or artery in the coronary artery tree.

This thesis proposes a data-driven method as a first attempt at annotating cardiac angiography based on deep learning. The method aims at automatically regressing segment points between different segments on the coronary artery tree as the annotation of the cardiac angiography. The proposed data-driven cardiac angiography annotation methods can learn and generalize from manually annotated cardiac angiography examples, but its performance is limited by the number and quality of examples for learning.