Automatic Separation and Classification of Arteries and Veins in Non-Contrast Thoracic CT Scans

J. Charbonnier, M. Brink, F. Ciompi, E. T. Scholten, C. M. Schaefer-Prokop and E. M. Van Rikxoort

in: Annual Meeting of the Radiological Society of North America, 2015

Abstract

PURPOSE Automated classification of pulmonary arteries and veins in thoracic CT scans is an unsolved problem which is important for e.g. CAD of pulmonary embolisms and treatment planning. This study presents and validates a new anatomy-based method to automatically classify arteries and veins in non-contrast chest CT scans. METHOD AND MATERIALS A set of 55 full inspiration non-contrast low dose chest CT scans (16x0.75mm, 120-140kVp, 30mAs) with variable severity of emphysema and interstitial lung diseases, were taken from a lung cancer screening trial. In all state-of-the-art vessel segmentation algorithms, arteries and veins are attached at locations where they cross, since these algorithms are not designed to distinguish between bifurcating and crossing vessels. This method starts with automatic vessel segmentation, followed by pruning the vessel segmentation to detect locations that are inconsistent with the topology of a tree structure. By disconnecting the vessels at these locations, the vessel segmentation is separated into subtrees that fulfill a tree structure and are assumed to be of an arterial or venous label. Next, subtrees are grouped using anatomical knowledge that arterial and venous capillaries meet each other at the alveoli, which implies that the corresponding peripheral arteries and veins go towards similar regions. By analyzing the peripheral vessels in each subtree, subtrees of the same artery-vein label are grouped without knowing the actual label. To extract the final artery-vein labels of the grouped subtrees, classification is performed using the fact that veins have an overall larger volume compared to arteries. For quantitative evaluation, two human observers manually labeled a total of 2750 randomly selected arteries and veins from all 55 scans. The accuracy and Cohen’s kappa between the observers and between the method and observers were used for evaluation. RESULTS Inter-observer Cohen’s kappa was 0.84 with 93% accuracy. The proposed method achieved a mean accuracy of 88% and a Cohen’s kappa of 0.76. CONCLUSION A new concept for artery-vein separation and classification was presented that uses anatomical information from peripheral arteries and veins. The performance of the presented method closely approximated the inter-observer agreement. CLINICAL RELEVANCE/APPLICATION Automatic artery-vein classification is essential for investigating pulmonary hypertension, COPD and for improving CAD systems for pulmonary embolisms.