Automatic segmentation of the aortic root in CT angiography of candidate patients for transcatheter aortic valve implantation

Transcatheter aortic valve implantation is a minimal-invasive intervention for implanting prosthetic valves in patients with aortic stenosis. Accurate automated sizing for planning and patient selection is expected to reduce adverse effects such as paravalvular leakage and stroke. Segmentation of th...

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Bibliographic Details
Published in:Medical & biological engineering & computing Vol. 52; no. 7; pp. 611 - 618
Main Authors: Elattar, M. A., Wiegerinck, E. M., Planken, R. N., vanbavel, E., van Assen, H. C., Baan, J., Marquering, H. A.
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.07.2014
Springer Nature B.V
Subjects:
Accuracy
Algorithms
Angiography - methods
Aortic stenosis
Aortic Valve - diagnostic imaging
Automation
Biomedical and Life Sciences
Biomedical Engineering and Bioengineering
Biomedicine
Computer Applications
Coronary vessels
Doppler effect
Engineering
Human Physiology
Humans
Image Processing, Computer-Assisted - methods
Imaging
Medical imaging
Medical research
Morphology
Original Article
Patients
Radiology
Reproducibility of Results
Studies
Tomography, X-Ray Computed - methods
Transcatheter Aortic Valve Replacement - methods
Transplants & implants
United States > US
CTA
Aortic root
Normalized cut
Medical image segmentation
TAVI
ISSN:0140-0118, 1741-0444, 1741-0444
Online Access:Get full text
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Summary:Transcatheter aortic valve implantation is a minimal-invasive intervention for implanting prosthetic valves in patients with aortic stenosis. Accurate automated sizing for planning and patient selection is expected to reduce adverse effects such as paravalvular leakage and stroke. Segmentation of the aortic root in CTA is pivotal to enable automated sizing and planning. We present a fully automated segmentation algorithm to extract the aortic root from CTA volumes consisting of a number of steps: first, the volume of interest is automatically detected, and the centerline through the ascending aorta and aortic root centerline are determined. Subsequently, high intensities due to calcifications are masked. Next, the aortic root is represented in cylindrical coordinates. Finally, the aortic root is segmented using 3D normalized cuts. The method was validated against manual delineations by calculating Dice coefficients and average distance error in 20 patients. The method successfully segmented the aortic root in all 20 cases. The mean Dice coefficient was 0.95 ± 0.03, and the mean radial absolute error was 0.74 ± 0.39 mm, where the interobserver Dice coefficient was 0.95 ± 0.03 and the mean error was 0.68 ± 0.34 mm. The proposed algorithm showed accurate results compared to manual segmentations.
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ISSN:0140-0118
1741-0444
1741-0444
DOI:10.1007/s11517-014-1165-7