Convolutional neural network based automatic plaque characterization for intracoronary optical coherence tomography images

Shenghua He, Jie Zheng, Akiko Maehara, Gary Mintz, Dalin Tang, Mark Anastasio, Hua Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Scopus citations

Abstract

Optical coherence tomography (OCT) can provide high-resolution cross-sectional images for analyzing superficial plaques in coronary arteries. Commonly, plaque characterization using intra-coronary OCT images is performed manually by expert observers. This manual analysis is time consuming and its accuracy heavily relies on the experience of human observers. Traditional machine learning based methods, such as the least squares support vector machine and random forest methods, have been recently employed to automatically characterize plaque regions in OCT images. Several processing steps, including feature extraction, informative feature selection, and final pixel classification, are commonly used in these traditional methods. Therefore, the final classification accuracy can be jeopardized by error or inaccuracy within each of these steps. In this study, we proposed a convolutional neural network (CNN) based method to automatically characterize plaques in OCT images. Unlike traditional methods, our method uses the image as a direct input and performs classification as a single- step process. The experiments on 269 OCT images showed that the average prediction accuracy of CNN-based method was 0.866, which indicated a great promise for clinical translation.

Original languageEnglish
Title of host publicationMedical Imaging 2018
Subtitle of host publicationImage Processing
EditorsElsa D. Angelini, Elsa D. Angelini, Bennett A. Landman
PublisherSPIE
ISBN (Electronic)9781510616370
DOIs
StatePublished - Jan 1 2018
EventMedical Imaging 2018: Image Processing - Houston, United States
Duration: Feb 11 2018Feb 13 2018

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume10574
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2018: Image Processing
CountryUnited States
CityHouston
Period02/11/1802/13/18

Keywords

  • Convolutional neural network
  • automatic plaque characterization
  • optical coherence tomography

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  • Cite this

    He, S., Zheng, J., Maehara, A., Mintz, G., Tang, D., Anastasio, M., & Li, H. (2018). Convolutional neural network based automatic plaque characterization for intracoronary optical coherence tomography images. In E. D. Angelini, E. D. Angelini, & B. A. Landman (Eds.), Medical Imaging 2018: Image Processing [1057432] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 10574). SPIE. https://doi.org/10.1117/12.2293957