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Proceedings Paper

Automated measurement of pulmonary artery in low-dose non-contrast chest CT images
Author(s): Yiting Xie; Mingzhu Liang; David F. Yankelevitz M.D.; Claudia I. Henschke M.D.; Anthony P. Reeves
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Paper Abstract

A new measurement of the pulmonary artery diameter is obtained where the artery may be robustly segmented between the heart and the artery bifurcation. An automated algorithm is presented that can make this pulmonary artery measurement in low-dose non-contrast chest CT images. The algorithm uses a cylinder matching method following geometric constraints obtained from other adjacent organs that have been previously segmented. This new measurement and the related ratio of pulmonary artery to aortic artery measurement are compared to traditional manual approaches for pulmonary artery characterization. The algorithm was qualitatively evaluated on 124 low-dose and 223 standard-dose non-contrast chest CT scans from two public datasets; 324 out of the 347 cases had good segmentations and in the other 23 cases there was significant boundary inaccuracy. For quantitative evaluation, the comparison was to manually marked pulmonary artery boundary in an axial slice in 45 cases; the resulting average Dice Similarity Coefficient was 0.88 (max 0.95, min 0.74). For the 45 cases with manual markings, the correlation between the automated pulmonary artery to ascending aorta diameter ratio and manual ratio at pulmonary artery bifurcation level was 0.81. Using Bland-Altman analysis, the mean difference of the two ratios was 0.03 and the limits of agreement was (-0.12, 0.18). This automated measurement may have utility as an alternative to the conventional manual measurement of pulmonary artery diameter at the bifurcation level especially in the context of noisy low-dose CT images.

Paper Details

Date Published: 20 March 2015
PDF: 9 pages
Proc. SPIE 9414, Medical Imaging 2015: Computer-Aided Diagnosis, 94141G (20 March 2015); doi: 10.1117/12.2081992
Show Author Affiliations
Yiting Xie, Cornell Univ. (United States)
Mingzhu Liang, Icahn School of Medicine at Mount Sinai (United States)
David F. Yankelevitz M.D., Icahn School of Medicine at Mount Sinai (United States)
Claudia I. Henschke M.D., Icahn School of Medicine at Mount Sinai (United States)
Anthony P. Reeves, Cornell Univ. (United States)

Published in SPIE Proceedings Vol. 9414:
Medical Imaging 2015: Computer-Aided Diagnosis
Lubomir M. Hadjiiski; Georgia D. Tourassi, Editor(s)

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