Automated identification of pulmonary arteries and veins depicted in non-contrast chest CT scans

• Published in: Medical image analysis Vol. 77; p. 102367
• Main Authors: Pu, Jiantao, Leader, Joseph K, Sechrist, Jacob, Beeche, Cameron A, Singh, Jatin P, Ocak, Iclal K, Risbano, Michael G
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.04.2022; Elsevier BV
• Subjects: Algorithms; Arteries; Artery; Artificial neural networks; Blood vessels; Chest; Computed tomography; Computer applications; Contrast agents; Contrast media; Deep learning; Differential geometry; Humans; Labeling; Lungs; Machine learning; Medical imaging; Neural networks; Neural Networks, Computer; Pulmonary arteries; Pulmonary artery; Pulmonary Artery - diagnostic imaging; Thorax; Tomography, X-Ray Computed - methods; Vein; Veins; Veins & arteries; Deep learning; Computed tomography; Vein; Differential geometry; Artery
• ISSN: 1361-8415; 1361-8423; 1361-8423
• DOI: 10.1016/j.media.2022.102367

•A novel strategy to efficiently and accurately identify pulmonary arteries and veins on non-contrast CT.•Segmentation of both intra- and extra-pulmonary arteries and veins.•The combination of a CNN-based method and a computational differential geometry method.•Demonstrate a very promising performance on 15 CT scans without iodinated contrast agents. We present a novel integrative computerized solution to automatically identify and differentiate pulmonary arteries and veins depicted on chest computed tomography (CT) without iodinated contrast agents. We first identified the central extrapulmonary arteries and veins using a convolutional neural network (CNN) model. Then, a computational differential geometry method was used to automatically identify the tubular-like structures in the lungs with high densities, which we believe are the intrapulmonary vessels. Beginning with the extrapulmonary arteries and veins, we progressively traced the intrapulmonary vessels by following their skeletons and differentiated them into arteries and veins. Instead of manually labeling the numerous arteries and veins in the lungs for machine learning, this integrative strategy limits the manual effort only to the large extrapulmonary vessels. We used a dataset consisting of 120 chest CT scans acquired on different subjects using various protocols to develop, train, and test the algorithms. Our experiments on an independent test set (n = 15) showed promising performance. The computer algorithm achieved a sensitivity of ∼98% in labeling the pulmonary artery and vein branches when compared with a human expert's results, demonstrating the feasibility of our computerized solution in pulmonary artery/vein labeling. [Display omitted]