Deep feature learning for automatic tissue classification of coronary artery using optical coherence tomography

Abdolmanafi, Atefeh, Duong, Luc, Dahdah, Nagib et Cheriet, Farida. 2017. « Deep feature learning for automatic tissue classification of coronary artery using optical coherence tomography ». Biomedical Optics Express, vol. 8, nº 2. pp. 1203-1220.
Compte des citations dans Scopus : 114.

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Résumé

Kawasaki disease (KD) is an acute childhood disease complicated by coronary artery aneurysms, intima thickening, thrombi, stenosis, lamellar calcifications, and disappearance of the media border. Automatic classification of the coronary artery layers (intima, media, and scar features) is important for analyzing optical coherence tomography (OCT) images recorded in pediatric patients. OCT has been known as an intracoronary imaging modality using near-infrared light which has recently been used to image the inner coronary artery tissues of pediatric patients, providing high spatial resolution (ranging from 10 to 20 μm). This study aims to develop a robust and fully automated tissue classification method by using the convolutional neural networks (CNNs) as feature extractor and comparing the predictions of three state-of-the-art classifiers, CNN, random forest (RF), and support vector machine (SVM). The results show the robustness of CNN as the feature extractor and random forest as the classifier with classification rate up to 96%, especially to characterize the second layer of coronary arteries (media), which is a very thin layer and it is challenging to be recognized and specified from other tissues.

Type de document:	Article publié dans une revue, révisé par les pairs
Mots-clés libres:	Fonds d'auteur ÉTS, FAETS
Professeur:	Professeur Duong, Luc
Affiliation:	Génie logiciel et des technologies de l'information
Date de dépôt:	03 févr. 2017 21:38
Dernière modification:	05 juin 2017 15:58
URI:	https://espace2.etsmtl.ca/id/eprint/14531

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