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Contour segmentation of the intima, media, and adventitia layers in intracoronary OCT images application to fully automatic detection of healthy wall regions


Zahnd, Guillaume; Hoogendoorn, Ayla; Combaret, Nicolas; Karanasos, Antonios; Péry, Emilie; Sarry, Laurent; Motreff, Pascal; Niessen, Wiro; Regar, Evelyn; van Soest, Gijs; Gijsen, Frank; van Walsum, Theo (2017). Contour segmentation of the intima, media, and adventitia layers in intracoronary OCT images application to fully automatic detection of healthy wall regions. International Journal of Computer Assisted Radiology and Surgery, 12(11):1923-1936.

Abstract

PURPOSE: Quantitative and automatic analysis of intracoronary optical coherence tomography images is useful and time-saving to assess cardiovascular risk in the clinical arena.
METHODS: First, the interfaces of the intima, media, and adventitia layers are segmented, by means of an original front propagation scheme, running in a 4D multi-parametric space, to simultaneously extract three non-crossing contours in the initial cross-sectional image. Second, information resulting from the tentative contours is exploited by a machine learning approach to identify healthy and diseased regions of the arterial wall. The framework is fully automatic.
RESULTS: The method was applied to 40 patients from two different medical centers. The framework was trained on 140 images and validated on 260 other images. For the contour segmentation method, the average segmentation errors were [Formula: see text] for the intima-media interface, [Formula: see text] for the media-adventitia interface, and [Formula: see text] for the adventitia-periadventitia interface. The classification method demonstrated a good accuracy, with a median Dice coefficient equal to 0.93 and an interquartile range of (0.78-0.98).
CONCLUSION: The proposed framework demonstrated promising offline performances and could potentially be translated into a reliable tool for various clinical applications, such as quantification of tissue layer thickness and global summarization of healthy regions in entire pullbacks.

Abstract

PURPOSE: Quantitative and automatic analysis of intracoronary optical coherence tomography images is useful and time-saving to assess cardiovascular risk in the clinical arena.
METHODS: First, the interfaces of the intima, media, and adventitia layers are segmented, by means of an original front propagation scheme, running in a 4D multi-parametric space, to simultaneously extract three non-crossing contours in the initial cross-sectional image. Second, information resulting from the tentative contours is exploited by a machine learning approach to identify healthy and diseased regions of the arterial wall. The framework is fully automatic.
RESULTS: The method was applied to 40 patients from two different medical centers. The framework was trained on 140 images and validated on 260 other images. For the contour segmentation method, the average segmentation errors were [Formula: see text] for the intima-media interface, [Formula: see text] for the media-adventitia interface, and [Formula: see text] for the adventitia-periadventitia interface. The classification method demonstrated a good accuracy, with a median Dice coefficient equal to 0.93 and an interquartile range of (0.78-0.98).
CONCLUSION: The proposed framework demonstrated promising offline performances and could potentially be translated into a reliable tool for various clinical applications, such as quantification of tissue layer thickness and global summarization of healthy regions in entire pullbacks.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > University Hospital Zurich > Clinic for Cardiovascular Surgery
Dewey Decimal Classification:610 Medicine & health
Uncontrolled Keywords:Optical coherence tomography, Coronary artery, Contour segmentation, Machine learning
Language:English
Date:November 2017
Deposited On:19 Dec 2017 15:56
Last Modified:01 Jan 2018 01:46
Publisher:Springer
ISSN:1861-6410
Free access at:PubMed ID. An embargo period may apply.
Publisher DOI:https://doi.org/10.1007/s11548-017-1657-7
PubMed ID:28801817

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