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Automated 3D segmentation and diameter measurement of the thoracic aorta on non-contrast enhanced CT

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OBJECTIVES: To develop and evaluate a fully automatic method to measure diameters of the ascending and descending aorta on non-ECG-gated, non-contrast computed tomography (CT) scans.

MATERIAL AND METHODS: The method combines multi-atlas registration to obtain seed points, aorta centerline extraction, and an optimal surface segmentation approach to extract the aorta surface around the centerline. From the extracted 3D aorta segmentation, the diameter of the ascending and descending aorta was calculated at cross-sectional slices perpendicular to the extracted centerline, at the level of the pulmonary artery bifurcation, and at 1-cm intervals up to 3 cm above and below this level. Agreement with manual annotations was evaluated by dice similarity coefficient (DSC) for segmentation overlap, mean surface distance (MSD), and intra-class correlation (ICC) of diameters on 100 CT scans from a lung cancer screening trial. Repeatability of the diameter measurements was evaluated on 617 baseline-one year follow-up CT scan pairs.

RESULTS: The agreement between manual and automatic segmentations was good with 0.95 ± 0.01 DSC and 0.56 ± 0.08 mm MSD. ICC between the diameters derived from manual and from automatic segmentations was 0.97, with the per-level ICC ranging from 0.87 to 0.94. An ICC of 0.98 for all measurements and per-level ICC ranging from 0.91 to 0.96 were obtained for repeatability.

CONCLUSION: This fully automatic method can assess diameters in the thoracic aorta reliably even in non-ECG-gated, non-contrast CT scans. This could be a promising tool to assess aorta dilatation in screening and in clinical practice.

KEY POINTS: • Fully automatic method to assess thoracic aorta diameters. • High agreement between fully automatic method and manual segmentations. • Method is suitable for non-ECG-gated CT and can therefore be used in screening.

Original languageEnglish
JournalEuropean Radiology
Issue number9
Pages (from-to)4613-4623
Number of pages11
Publication statusPublished - Sep 2019

    Research areas

  • Aged, Algorithms, Aorta/anatomy & histology, Aorta, Thoracic/anatomy & histology, Computed Tomography Angiography/methods, Cross-Sectional Studies, Female, Humans, Imaging, Three-Dimensional/methods, Male, Middle Aged, Radiographic Image Interpretation, Computer-Assisted/methods, Reproducibility of Results, Tomography, X-Ray Computed/methods

ID: 59439261