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

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 automaticall...

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Bibliographic Details
Main Authors: Nardelli, P., Jimenez-Carretero, D., Bermejo-Pelaez, D., Ledesma-Carbayo, M.J., Rahaghi, Farbod N., San Jose Estepar, R.
Format: Conference Proceeding
Language:English
Subjects:
Arteries
Artery-vein segmentation
Computed tomography
convolutional neural networks
Frangi filter
Image segmentation
lung
Lungs
Manuals
Radio frequency
Veins
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Summary: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.
ISSN:1945-7928
1945-8452
DOI:10.1109/ISBI.2017.7950543