Open Access

ARTICLE

Convolution Neural Networks and Support Vector Machines for Automatic Segmentation of Intracoronary Optical Coherence Tomography

by Caining Zhang, Huaguang Li, Xiaoya Guo, David Molony, Xiaopeng Guo, Habib Samady, Don P. Giddens, Lambros Athanasiou, Rencan Nie, Jinde Cao, Dalin Tang

1 School of Biological Science & Medical Engineering, Southeast University, Nanjing, 210096, China.
2 School of Information Science & Engineering, Yunnan University, Kunming, 650091, China.
3 School of Mathematics, Southeast University, Nanjing, 210096, China.
4 Department of Medicine, Emory University School of Medicine, Atlanta, GA, 30307, USA.
5 The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, 30332 USA.
6 Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA 02139 USA.
7 Mathematical Sciences Department, Worcester Polytechnic Institute, Worcester, MA 01609 USA.
* Corresponding Authors: Dalin Tang, Southeast University, China. Email: dtang@wpi.edu; Rencan Nei. Email: rcnie@ynu.edu.cn; Jinde Cao. Email: jdcao@seu.edu.cn.

Molecular & Cellular Biomechanics 2019, 16(2), 153-161. https://doi.org/10.32604/mcb.2019.06873

Abstract

Cardiovascular diseases are closely associated with deteriorating atherosclerotic plaques. Optical coherence tomography (OCT) is a recently developed intravascular imaging technique with high resolution approximately 10 microns and could provide accurate quantification of coronary plaque morphology. However, tissue segmentation of OCT images in clinic is still mainly performed manually by physicians which is time consuming and subjective. To overcome these limitations, two automatic segmentation methods for intracoronary OCT image based on support vector machine (SVM) and convolutional neural network (CNN) were performed to identify the plaque region and characterize plaque components. In vivo IVUS and OCT coronary plaque data from 5 patients were acquired at Emory University with patient’s consent obtained. Seventy-seven matched IVUS and OCT slices with good image quality and lipid cores were selected for this study. Manual OCT segmentation was performed by experts using virtual histology IVUS as guidance, and used as gold standard in the automatic segmentations. The overall classification accuracy based on CNN method achieved 95.8%, and the accuracy based on SVM was 71.9%. The CNN-based segmentation method can better characterize plaque compositions on OCT images and greatly reduce the time spent by doctors in segmenting and identifying plaques.

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