Semiautomatic, Quantitative Measurement of Aortic Valve Area Using CTA: Validation and Comparison with Transthoracic Echocardiography

Objective. The aim of this work was to develop a fast and robust (semi)automatic segmentation technique of the aortic valve area (AVA) MDCT datasets. Methods. The algorithm starts with detection and cropping of Sinus of Valsalva on MPR image. The cropped image is then binarized and seed points are m...

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Bibliographic Details
Published in:BioMed research international 2015-01, Vol.2015 (2015), p.1-9
Main Authors: Leiner, T., Budde, R. P. J., Van Ooijen, Peter M., Prakken, N., Tuncay, V., Oudkerk, Matthijs
Format: Article
Language:English
Subjects:
Aged, 80 and over
Algorithms
Aortic valve
Aortic Valve - diagnostic imaging
Automation
Calcification
Cardiology
Colleges & universities
Coronary vessels
CT imaging
Echocardiography
Echocardiography - methods
Female
Humans
Image detection
Image Processing, Computer-Assisted
Male
Manuals
Mathematical analysis
Measurement
Measurement techniques
Medical imaging
Mortality
Observer Variation
Patients
Segmentation
Shape
Software
Time Factors
Tomography
Tomography, X-Ray Computed - methods
Valves
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Summary:Objective. The aim of this work was to develop a fast and robust (semi)automatic segmentation technique of the aortic valve area (AVA) MDCT datasets. Methods. The algorithm starts with detection and cropping of Sinus of Valsalva on MPR image. The cropped image is then binarized and seed points are manually selected to create an initial contour. The contour moves automatically towards the edge of aortic AVA to obtain a segmentation of the AVA. AVA was segmented semiautomatically and manually by two observers in multiphase cardiac CT scans of 25 patients. Validation of the algorithm was obtained by comparing to Transthoracic Echocardiography (TTE). Intra- and interobserver variability were calculated by relative differences. Differences between TTE and MDCT manual and semiautomatic measurements were assessed by Bland-Altman analysis. Time required for manual and semiautomatic segmentations was recorded. Results. Mean differences from TTE were −0.19 (95% CI: −0.74 to 0.34) cm2 for manual and −0.10 (95% CI: −0.45 to 0.25) cm2 for semiautomatic measurements. Intra- and interobserver variability were 8.4 ± 7.1% and 27.6 ± 16.0% for manual, and 5.8 ± 4.5% and 16.8 ± 12.7% for semiautomatic measurements, respectively. Conclusion. Newly developed semiautomatic segmentation provides an accurate, more reproducible, and faster AVA segmentation result.
ISSN:2314-6133
2314-6141
DOI:10.1155/2015/648283