Automatic Quantification of the Aorta and Pulmonary Artery in Chest CT: methods and validation in lung screening

Cardiovascular diseases and Chronic Obstructive Pulmonary Disease (COPD) are among the major leading causes of death globally. In the search for early identification of individuals at risk of cardiovascular disease in COPD, imaging-based assessments of the shape and size of the aorta and pulmonary artery have rapidly gained interest. Changes in these two large arteries may indicate cardiovascular diseases such as pulmonary hypertension and aortic aneurysm. Furthermore, the ratio of the diameter of the pulmonary artery to ascending aorta at the level of pulmonary artery bifurcation is shown to be associated with an increased risk of severe exacerbations and increased mortality in patients with COPD. Therefore, it is essential to accurately delineate and quantify the anatomy of the aorta and pulmonary artery. With the growing use of low-dose non-contrast thoracic CT scans for lung cancer screening, there is an opportunity to measure the aorta and pulmonary artery in these scans. However, performing diameter measurements manually is laborintensive; therefore, automatic 3D segmentation and measurement techniques are desirable. This thesis develops and validates fully automatic segmentation and diameter measurement techniques to quantify the shape and size of aorta and pulmonary arteries in CT scans. It presents a method based on optimal surface graph cuts to segment the aorta and pulmonary arteries separately and extract landmarks for each vessel for automatic diameter measurement. It also presents a new deep-learning-based approuch named Posterior-CRF, for jointly segmenting the vessels. The methods present robust and reproducible results of sufficient accuracy and reliability for use in the clinical study.