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Updated: Jun 13, 2026
Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
Published on: December 19, 2020
Segmentation and quantification of pulmonary artery for noninvasive CT assessment of sickle cell secondary pulmonary
Marius George Linguraru1, John A Pura, Robert L Van Uitert
1Radiology and Imaging Sciences, Clinical Center National Institutes of Health, Bethesda, Maryland 20892, USA. lingurarum@mail.nih.gov
Purpose:
Pulmonary arterial hypertension (PAH) is a progressive vascular disease that results in high mortality and morbidity in sickle cell disease (SCD) patients. PAH diagnosis is invasive via right heart catheterization, but manual measurements of the main pulmonary artery (PA) diameters from computed tomography (CT) have shown promise as noninvasive surrogate marker of PAH. The authors propose a semiautomated computer-assisted diagnostic (CAD) tool to quantify the main PA size from pulmonary CT angiography (CTA).
Methods:
A follow-up retrospective study investigated the potential of CT and image analysis to quantify the presence of PAH secondary to SCD based on PA size. The authors segmented the main pulmonary arteries using a combination of fast marching level sets and geodesic active contours from smoothed pulmonary CTA images of 20 SCD patients with proven PAH by right heart catheterization and 20 matched negative controls. From the PA segmentation, a Euclidean distance map was calculated and an algorithm based on fast marching methods was used to compute subvoxel precise centerlines of the PA trunk (PT) and main left/right PA (PM). Maximum distentions of PT and PM were automatically quantified using the centerline and validated with manual measurements from two observers.
Results:
The pulmonary trunk and main were significantly larger (p < 0.001) in PAH/SCD patients (33.73 +/- 3.92 mm for PT and 25.17 +/- 2.90 for PM) than controls (27.03 +/- 2.94 mm for PT and 20.62 +/- 3.06 for PM). The discrepancy was qualitatively improved when vessels' diameters were normalized by body surface area (p < 0.001). The validation of the method showed high correlation (mean R=0.9 for PT and R = 0.91 for PM) and Bland-Altman agreement (0.4 +/- 3.6 mm for PT and 0.5 +/- 2.9 mm for PM) between CAD and manual measurements. Quantification errors were comparable to intraobserver and interobserver variability. CAD measurements between two different users were robust and reproducible with correlations of R = 0.99 for both PT and PM and Bland-Altman agreements of -0.13 +/- 1.33 mm for PT and -0.08 +/- 0.84 mm for PM.
Conclusion:
Results suggest that the semiautomated quantification of pulmonary artery has sufficient accuracy and reproducibility for clinical use. CT with image processing and extraction of PA biomarkers show great potential as a surrogate indicator for diagnosis or quantification of PAH, and could be an important tool for drug discovery and noninvasive clinical surveillance.
