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Computer-aided detection of intracoronary stent in intravascular ultrasound sequences

Computer-aided detection of intracoronary stent in intravascular ultrasound sequences Purpose: An intraluminal coronary stent is a metal mesh tube deployed in a stenotic artery during percutaneous coronary intervention (PCI), in order to prevent acute vessel occlusion. The identification of struts location and the definition of the stent shape is relevant for PCI planning and for patient follow-up. The authors present a fully automatic framework for computer-aided detection (CAD) of intracoronary stents in intravascular ultrasound (IVUS) image sequences. The CAD system is able to detect stent struts and estimate the stent shape. Methods: The proposed CAD uses machine learning to provide a comprehensive interpretation of the local structure of the vessel by means of semantic classification. The output of the classification stage is then used to detect struts and to estimate the stent shape. The proposed approach is validated using a multicentric data-set of 1,015 images from 107 IVUS sequences containing both metallic and bioabsorbable stents. Results: The method was able to detect struts in both metallic stents with an overall F-measure of 77.7% and a mean distance of 0.15 mm from manually annotated struts, and in bioabsorbable stents with an overall F-measure of 77.4% and a mean distance of 0.09 mm from manually annotated struts. Conclusions: The results are close to the interobserver variability and suggest that the system has the potential of being used as a method for aiding percutaneous interventions. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Medical Physics American Association of Physicists in Medicine

Computer-aided detection of intracoronary stent in intravascular ultrasound sequences

Medical Physics , Volume 43 (10) – Oct 1, 2016

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References (33)

Publisher
American Association of Physicists in Medicine
Copyright
Copyright © 2016 Author(s)
ISSN
0094-2405
DOI
10.1118/1.4962927
pmid
27782708
Publisher site
See Article on Publisher Site

Abstract

Purpose: An intraluminal coronary stent is a metal mesh tube deployed in a stenotic artery during percutaneous coronary intervention (PCI), in order to prevent acute vessel occlusion. The identification of struts location and the definition of the stent shape is relevant for PCI planning and for patient follow-up. The authors present a fully automatic framework for computer-aided detection (CAD) of intracoronary stents in intravascular ultrasound (IVUS) image sequences. The CAD system is able to detect stent struts and estimate the stent shape. Methods: The proposed CAD uses machine learning to provide a comprehensive interpretation of the local structure of the vessel by means of semantic classification. The output of the classification stage is then used to detect struts and to estimate the stent shape. The proposed approach is validated using a multicentric data-set of 1,015 images from 107 IVUS sequences containing both metallic and bioabsorbable stents. Results: The method was able to detect struts in both metallic stents with an overall F-measure of 77.7% and a mean distance of 0.15 mm from manually annotated struts, and in bioabsorbable stents with an overall F-measure of 77.4% and a mean distance of 0.09 mm from manually annotated struts. Conclusions: The results are close to the interobserver variability and suggest that the system has the potential of being used as a method for aiding percutaneous interventions.

Journal

Medical PhysicsAmerican Association of Physicists in Medicine

Published: Oct 1, 2016

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