Open AccessAutomated plaque characterization using deep learning on coronary intravascular optical coherence tomographic images
Author(s) -
Juhwan Lee,
David Prabhu,
Chaitanya Kolluru,
Yazan Gharaibeh,
Vladislav N. Zimin,
Hiram G. Bezerra,
David L. Wilson
Publication year - 2019
Publication title -
biomedical optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.10.006497
Subject(s) - optical coherence tomography , artificial intelligence , deep learning , computer science , ground truth , segmentation , vulnerable plaque , pattern recognition (psychology) , radiology , medicine , computer vision , pathology
Accurate identification of coronary plaque is very important for cardiologists when treating patients with advanced atherosclerosis. We developed fully-automated semantic segmentation of plaque in intravascular OCT images. We trained/tested a deep learning model on a folded, large, manually annotated clinical dataset. The sensitivities/specificities were 87.4%/89.5% and 85.1%/94.2% for pixel-wise classification of lipidous and calcified plaque, respectively. Automated clinical lesion metrics, potentially useful for treatment planning and research, compared favorably (<4%) with those derived from ground-truth labels. When we converted the results to A-line classification, they were significantly better (p < 0.05) than those obtained previously by using deep learning classifications of A-lines.