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Characterization of coronary artery pathological formations from OCT imaging using deep learning


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Abdolmanafi, Atefeh, Duong, Luc, Dahdah, Nagib, Adib, Ibrahim Ragui et Cheriet, Farida. 2018. « Characterization of coronary artery pathological formations from OCT imaging using deep learning ». Biomedical Optics Express, vol. 9, nº 10. pp. 4936-4960.
Compte des citations dans Scopus : 16.

Duong L 2018 17554 Characterization of coronary artery pathological.pdf - Published Version

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Coronary artery disease is the number one health hazard leading to the pathological formations in coronary artery tissues. In severe cases, they can lead to myocardial infarction and sudden death. Optical Coherence Tomography (OCT) is an interferometric imaging modality, which has been recently used in cardiology to characterize coronary artery tissues providing high resolution ranging from 10 to 20 µm. In this study, we investigate different deep learning models for robust tissue characterization to learn the various intracoronary pathological formations caused by Kawasaki disease (KD) from OCT imaging. The experiments are performed on 33 retrospective cases comprising of pullbacks of intracoronary cross-sectional images obtained from different pediatric patients with KD. Our approach evaluates deep features computed from three different pre-trained convolutional networks. Then, a majority voting approach is applied to provide the final classification result. The results demonstrate high values of accuracy, sensitivity, and specificity for each tissue (up to 0.99 ± 0.01). Hence, deep learning models and especially, majority voting method are robust for automatic interpretation of the OCT images.

Item Type: Peer reviewed article published in a journal
Duong, Luc
Affiliation: Génie logiciel et des technologies de l'information
Date Deposited: 19 Nov 2018 20:19
Last Modified: 29 Nov 2018 14:07

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