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...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 52; no. 7; pp. 611 - 619 |
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| Autores principales: | , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Jul2014
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=103829326&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 103829326 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Jul2014 vid: 52 iid: 7 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 103829326 NLM24903606 2012619466 10.1007/s11517-014-1165-7 NLM24903606 103829326 ppf: 611 ppct: 8 formats: fmt: @attributes: type: P tig: atl: Automatic segmentation of the aortic root in CT angiography of candidate patients for transcatheter aortic valve implantation. aug: au: Elattar, M A Wiegerinck, E M Planken, R N Vanbavel, E van Assen, H C Baan Jr, J Marquering, H A Baan, J Jr affil: Department of Biomedical Engineering and Physics, Academic Medical Center, University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands, mustafa.elattar@gmail.com. sug: subj: Angiography Methods Aortic Valve Radiography Image Processing, Computer Assisted Methods Tomography, X-Ray Computed Methods Heart Valve Prosthesis Methods Algorithms Human Reproducibility of Results ab: 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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