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• W2990936018 abstract "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." @default.
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• W2990936018 date "2019-11-25" @default.
• W2990936018 modified "2023-10-12" @default.
• W2990936018 title "Automated plaque characterization using deep learning on coronary intravascular optical coherence tomographic images" @default.
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• W2990936018 doi "https://doi.org/10.1364/boe.10.006497" @default.
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