A deep learning approach to classify atherosclerosis using intracoronary optical coherence tomography
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109500N.pdf
Description
Published version
Size
729.55 KB
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Checksum (MD5)
26f932b3cbb708d22434a92939d6bb5b
Author(s)
Athanasiou, Lambros S.
Olender, Max
Edelman, Elazer R
Date Issued
March 2019
Journal
Medical Imaging 2019: Computer-Aided Diagnosis
Publisher
SPIE
Citation
Athanasiou, Lambros S. et al. “A deep learning approach to classify atherosclerosis using intracoronary optical coherence tomography.” Medical Imaging 2019: Computer-Aided Diagnosis, vol. 10950 © 2019 The Author(s)
Version
Final published version
Abstract
Optical coherence tomography (OCT) is a fiber-based intravascular imaging modality that produces high-resolution tomographic images of artery lumen and vessel wall morphology. Manual analysis of the diseased arterial wall is time consuming and sensitive to inter-observer variability; therefore, machine-learning methods have been developed to automatically detect and classify mural composition of atherosclerotic vessels. However, none of the tissue classification methods include in their analysis the outer border of the OCT vessel, they consider the whole arterial wall as pathological, and they do not consider in their analysis the OCT imaging limitations, e.g. shadowed areas. The aim of this study is to present a deep learning method that subdivides the whole arterial wall into six different classes: calcium, lipid tissue, fibrous tissue, mixed tissue, non-pathological tissue or media, and no visible tissue. The method steps include defining wall area (WAR) using previously developed lumen and outer border detection methods, and automatic characterization of the WAR using a convolutional neural network (CNN) algorithm. To validate this approach, 700 images of diseased coronary arteries from 28 patients were manually annotated by two medical experts, while the non-pathological wall and media was automatically detected based on the Euclidian distance of the lumen to the outer border of the WAR. Using the proposed method, an overall classification accuracy 96% is reported, indicating great promise for clinical translation.
MIT Department
Massachusetts Institute of Technology. Institute for Medical Engineering & Science
Massachusetts Institute of Technology. Department of Mechanical Engineering
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DOI of Published Version
https://doi.org/10.1117/12.2513078
