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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Veröffentlicht in:	Medical & biological engineering & computing Jg. 52; H. 7; S. 611 - 618
Hauptverfasser:	Elattar, M. A., Wiegerinck, E. M., Planken, R. N., vanbavel, E., van Assen, H. C., Baan, J., Marquering, H. A.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.07.2014 Springer Nature B.V
Schlagworte:	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
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Abstract	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.
AbstractList	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. 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. 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 plus or minus 0.03, and the mean radial absolute error was 0.74 plus or minus 0.39 mm, where the interobserver Dice coefficient was 0.95 plus or minus 0.03 and the mean error was 0.68 plus or minus 0.34mm. The proposed algorithm showed accurate results compared to manual segmentations. 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.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. 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 ± 0.34mm. The proposed algorithm showed accurate results compared to manual segmentations.[PUBLICATION ABSTRACT]
Author	Marquering, H. A. Elattar, M. A. vanbavel, E. Planken, R. N. Baan, J. van Assen, H. C. Wiegerinck, E. M.
Author_xml	– sequence: 1 givenname: M. A. surname: Elattar fullname: Elattar, M. A. email: mustafa.elattar@gmail.com, M.Elattar@amc.nl organization: Department of Biomedical Engineering and Physics, Academic Medical Center, University of Amsterdam – sequence: 2 givenname: E. M. surname: Wiegerinck fullname: Wiegerinck, E. M. organization: Department of Cardiology, Academic Medical Center, University of Amsterdam – sequence: 3 givenname: R. N. surname: Planken fullname: Planken, R. N. organization: Department of Radiology, Academic Medical Center, University of Amsterdam – sequence: 4 givenname: E. surname: vanbavel fullname: vanbavel, E. organization: Department of Biomedical Engineering and Physics, Academic Medical Center, University of Amsterdam – sequence: 5 givenname: H. C. surname: van Assen fullname: van Assen, H. C. organization: Department of Electrical Engineering, Eindhoven University of Technology – sequence: 6 givenname: J. surname: Baan fullname: Baan, J. organization: Department of Cardiology, Academic Medical Center, University of Amsterdam – sequence: 7 givenname: H. A. surname: Marquering fullname: Marquering, H. A. organization: Department of Biomedical Engineering and Physics, Academic Medical Center, University of Amsterdam, Department of Radiology, Academic Medical Center, University of Amsterdam
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/24903606$$D View this record in MEDLINE/PubMed
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Snippet	Transcatheter aortic valve implantation is a minimal-invasive intervention for implanting prosthetic valves in patients with aortic stenosis. Accurate...
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SubjectTerms	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
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Title	Automatic segmentation of the aortic root in CT angiography of candidate patients for transcatheter aortic valve implantation
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