Deep-learning strategy for pulmonary artery-vein classification of non-contrast CT images

• Published in: 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) Vol. 2017; pp. 384 - 387
• Main Authors: Nardelli, P., Jimenez-Carretero, D., Bermejo-Pelaez, D., Ledesma-Carbayo, M.J., Rahaghi, Farbod N., San Jose Estepar, R.
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 01-04-2017
• Subjects: Arteries; Artery-vein segmentation; Computed tomography; convolutional neural networks; Frangi filter; Image segmentation; lung; Lungs; Manuals; Radio frequency; Veins; lung; Artery-vein segmentation; convolutional neural networks; Frangi filter
• ISSN: 1945-7928; 1945-8452
• DOI: 10.1109/ISBI.2017.7950543

Artery-vein classification on pulmonary computed tomography (CT) images is becoming of high interest in the scientific community due to the prevalence of pulmonary vascular disease that affects arteries and veins through different mechanisms. In this work, we present a novel approach to automatically segment and classify vessels from chest CT images. We use a scale-space particle segmentation to isolate vessels, and combine a convolutional neural network (CNN) to graph-cut (GC) to classify the single particles. Information about proximity of arteries to airways is learned by the network by means of a bronchus enhanced image. The methodology is evaluated on the superior and inferior lobes of the right lung of twenty clinical cases. Comparison with manual classification and a Random Forests (RF) classifier is performed. The algorithm achieves an overall accuracy of 87% when compared to manual reference, which is higher than the 73% accuracy achieved by RF.