  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.
