Limits...
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 SOV detection and cropping algorithm.
© Copyright Policy - open-access
Related In: Results  -  Collection


getmorefigures.php?uid=PMC4499628&req=5

fig2: The flowchart of the SOV detection and cropping algorithm.

Mentions: Image cropping was used to reduce computation time. In the object detection the object size, shape, location, and orientation play major roles. Since the SOV is located in the central part of the MPR image, detection and cropping of this region begin with a preliminary cropping operation, which covers the most of the central part of the MPR image. After the initial cropping, the grayscale image is binarized using global thresholding with a threshold level based on the histogram of the image. The binary image contains only white (object) and black (background) pixels, which enables detection of the objects in the image and facilitates the use of morphological operations. Following binarization, objects smaller than 700 pixels were removed. Secondly the SOV was disconnected and isolated. The SOV is located in the central part of image, such that objects on the border of the image were removed. After detection of the SOV, the region covering it was cropped from the image (Figure 2). All following operations were performed on the cropped image.


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 SOV detection and cropping algorithm.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig2: The flowchart of the SOV detection and cropping algorithm.
Mentions: Image cropping was used to reduce computation time. In the object detection the object size, shape, location, and orientation play major roles. Since the SOV is located in the central part of the MPR image, detection and cropping of this region begin with a preliminary cropping operation, which covers the most of the central part of the MPR image. After the initial cropping, the grayscale image is binarized using global thresholding with a threshold level based on the histogram of the image. The binary image contains only white (object) and black (background) pixels, which enables detection of the objects in the image and facilitates the use of morphological operations. Following binarization, objects smaller than 700 pixels were removed. Secondly the SOV was disconnected and isolated. The SOV is located in the central part of image, such that objects on the border of the image were removed. After detection of the SOV, the region covering it was cropped from the image (Figure 2). All following operations were performed on the cropped image.

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.