Automated segmentation of the pulmonary arteries in low-dose CT by vessel tracking

TL;DR

This paper introduces a fully automated method for segmenting pulmonary arteries in low-dose CT scans, improving accuracy and efficiency for lung nodule detection by tracking vessels from the hilum into the lungs.

Contribution

The study presents a novel vessel tracking algorithm combined with a sparse surface evaluation metric for accurate, automated pulmonary artery segmentation in low-dose CT images.

Findings

Correctly segmented 134 out of 210 arteries

Achieved 90% specificity for arteries

Average segmentation error of 0.15 mm

Abstract

We present a fully automated method for top-down segmentation of the pulmonary arterial tree in low-dose thoracic CT images. The main basal pulmonary arteries are identified near the lung hilum by searching for candidate vessels adjacent to known airways, identified by our previously reported airway segmentation method. Model cylinders are iteratively fit to the vessels to track them into the lungs. Vessel bifurcations are detected by measuring the rate of change of vessel radii, and child vessels are segmented by initiating new trackers at bifurcation points. Validation is accomplished using our novel sparse surface (SS) evaluation metric. The SS metric was designed to quantify the magnitude of the segmentation error per vessel while significantly decreasing the manual marking burden for the human user. A total of 210 arteries and 205 veins were manually marked across seven test cases. 134/210 arteries were correctly segmented, with a specificity for arteries of 90%, and average segmentation error of 0.15 mm. This fully-automated segmentation is a promising method for improving lung nodule detection in low-dose CT screening scans, by separating vessels from surrounding iso-intensity objects.