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

Tuncay V, Prakken N, van Ooijen PM, Budde RP, Leiner T, Oudkerk M - Biomed Res Int (2015)

Bottom Line: Time required for manual and semiautomatic segmentations was recorded.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.Newly developed semiautomatic segmentation provides an accurate, more reproducible, and faster AVA segmentation result.

View Article: PubMed Central - PubMed

Affiliation: Center for Medical Imaging North East Netherlands (CMI-NEN), Department of Radiology, University of Groningen, University Medical Center Groningen, P.O. Box 30001, 9700 RB Groningen, Netherlands.

ABSTRACT

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) cm(2) for manual and -0.10 (95% CI: -0.45 to 0.25) cm(2) 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.

No MeSH data available.


The flowchart of the general algorithm.
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Related In: Results  -  Collection


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fig1: The flowchart of the general algorithm.

Mentions: The segmentation algorithm (Figure 1) starts with the detection of the Sinus of Valsalva (SOV) on the MPR image. Once the SOV is detected, the region covering the SOV is cropped from the whole image (Figure 1(a)). The cropped image (Figure 1(b)) is binarized by adaptive thresholding (Figure 1(c)). The user places seed points (Figure 1(d)) to create an initial contour (Figure 1(e)) covering the aortic valve opening. The contour moves towards the edge of the aortic valve opening automatically (Figure 1(f)). The contour covers the pixels from the edge of the opening area and also the AVA opening area. The pixels of the opening area are selected and the number of pixels is multiplied by the pixel size to determine the AV opening area (Figure 1(g)).


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

Tuncay V, Prakken N, van Ooijen PM, Budde RP, Leiner T, Oudkerk M - Biomed Res Int (2015)

The flowchart of the general algorithm.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4499628&req=5

fig1: The flowchart of the general algorithm.
Mentions: The segmentation algorithm (Figure 1) starts with the detection of the Sinus of Valsalva (SOV) on the MPR image. Once the SOV is detected, the region covering the SOV is cropped from the whole image (Figure 1(a)). The cropped image (Figure 1(b)) is binarized by adaptive thresholding (Figure 1(c)). The user places seed points (Figure 1(d)) to create an initial contour (Figure 1(e)) covering the aortic valve opening. The contour moves towards the edge of the aortic valve opening automatically (Figure 1(f)). The contour covers the pixels from the edge of the opening area and also the AVA opening area. The pixels of the opening area are selected and the number of pixels is multiplied by the pixel size to determine the AV opening area (Figure 1(g)).

Bottom Line: Time required for manual and semiautomatic segmentations was recorded.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.Newly developed semiautomatic segmentation provides an accurate, more reproducible, and faster AVA segmentation result.

View Article: PubMed Central - PubMed

Affiliation: Center for Medical Imaging North East Netherlands (CMI-NEN), Department of Radiology, University of Groningen, University Medical Center Groningen, P.O. Box 30001, 9700 RB Groningen, Netherlands.

ABSTRACT

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) cm(2) for manual and -0.10 (95% CI: -0.45 to 0.25) cm(2) 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.

No MeSH data available.