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

Abstract

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.

Keywords: Artery; Computed tomography; Deep learning; Differential geometry; Vein.

Figure 1.

Illustration of pulmonary arteries and veins. (a) Contrast-enhanced chest CT scan showing left upper lobe vessels; (b) The arteries, veins, and airways labeled manually; (c) The intrapulmonary vessels without differentiation of the arteries and veins; and (d) Inclusion of the extrapulmonary artery and vein.

Figure 2.

Schematic flowchart for identifying pulmonary arteries and veins.

Figure 3.

The implementation of the CNN-based segmentation scheme.

Figure 4.

Illustration of the developed computational geometry solution for identifying intrapulmonary vessels. (a) a CT scan, (b) the 3-D surface model of the local region as indicated by the box in (a), (c) the local enlargement of the surface model in (b), (d) the surface model after the application of the Laplacian smoothing, (e) the local enlargement of the surface model in (d), (f) the surface model after filtering the triangles based on the principal curvature analyses, (g) the filtered surface model, (h) the surface model after picking up the small patches from (g), and (i) the final filtered surface model after the pick-up operation.

Figure 5.

Intrapulmonary vessels identified using the developed differential geometry method and multiple thresholds [−700 HU, −200 HU]. (a) original CT image, (b) segmented lung, (c) intrapulmonary vessels detected, and (d) 3-D visualization of the detected vessels.

Figure. 6.

Illustration of the procedure for differentiating the intrapulmonary arteries and veins. (a) the merge of the intra- and extra-pulmonary vessels, (b) the skeletonization of the intra-pulmonary vessels, (c) the differentiation of the intra-pulmonary vessel skeletons, and (d) the final differentiation of the lung arteries and veins.

Figure 7.

An example to illustrate the challenge of tracing the lung arteries and veins. (a) the 3-D surface model of the lung vessels in a local region, (b) the local enlargement of the region as indicated by the box in (a), (c) the skeletons of the vessel branches in (a) and each skeleton indicates a vessel branch, and (d) the highlight of the two skeletons (i.e., A and B) associated with the challenge of vessel tracing. Arrow C illustrated a disconnected branch as the result of skeletonization. In (c), “1”–”6” indicated four different branches.

Figure 8.

Separating extrapulmonary vessels (a) into two parts based on whether the vessels are within the lung volume (b), including (1) mediastinal vessels (red) and (2) hilum vessels (blue) in (c).

Figure 9.

The vessels identified by the computerized scheme (a) and the manual operation (b). The regions in green were vessel branches identified by the computerized scheme but not labeled by the radiologist.

Figure 10.

Visualization of the computerized results. (a) the global view of the lung artery and vein labeling, (b) the view of the right lung, and (c) the local enlargement of the region as indicated by the box in (b).

Figure 11.

The computerized results and radiologist’s manual segmentation of the arteries and veins. (a) CT image of the lungs in the coronal view, image thickness of 1.25 mm. (b) 3-D visualization of the computer’s segmentation. (c) 3-D visualization of the radiologist’s segmentation. (d) Overlay of the computer’s segmentation on the CT image. (e) Overlay of the radiologist’s segmentation on the CT image.

Figure 12.

The computerized results and radiologist’s manual segmentation of the arteries and veins. (a) CT image of the lungs in the coronal view, image thickness of 1.5 mm. (b) 3-D visualization of the computer’s segmentation. (c) 3-D visualization of the radiologist’s segmentation. (d) Overlay of the computer’s segmentation on the CT image. (e) Overlay of the radiologist’s segmentation on the CT image.

Figure 13.

An example illustrating the incorrect differentiation of intrapulmonary arteries and veins. (a) the arteries and veins labeled by the computerized scheme, where the vessels indicated by arrow A were incorrectly labeled as the follow-up branch of the vessel as indicated by arrow B, (b) the local enlargement of the regions indicated by A and B, and (c) the arteries and veins labeled by the radiologist.