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

被引:105
作者
Abdolmanafi, Atefeh [1 ]
Duong, Luc [1 ]
Dahdah, Nagib [2 ,3 ]
Cheriet, Farida [4 ]
机构
[1] Ecole Technol Super, Dept Software & IT Engn, Montreal, PQ, Canada
[2] Ctr Hosp Univ St Justine, Div Pediat Cardiol, Montreal, PQ, Canada
[3] Ctr Hosp Univ St Justine, Res Ctr, Montreal, PQ, Canada
[4] Ecole Polytech, Dept Comp Engn, Montreal, PQ, Canada
来源
BIOMEDICAL OPTICS EXPRESS | 2017年 / 8卷 / 02期
关键词
KAWASAKI-DISEASE; DOMAIN;
D O I
10.1364/BOE.8.001203
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
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 mu m). 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. (C) 2017 Optical Society of America
引用
收藏
页码:1203 / 1220
页数:18
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