@Article{cmes.2020.09718,
AUTHOR = {Caining Zhang, Xiaopeng Guo, Xiaoya Guo, David Molony, Huaguang Li, Habib Samady, Don P. Giddens, Lambros Athanasiou, Dalin Tang, Rencan Nie, Jinde Cao},
TITLE = {Machine Learning Model Comparison for Automatic Segmentation of Intracoronary Optical Coherence Tomography and Plaque Cap Thickness Quantification},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {123},
YEAR = {2020},
NUMBER = {2},
PAGES = {631--646},
URL = {http://www.techscience.com/CMES/v123n2/38693},
ISSN = {1526-1506},
ABSTRACT = {Optical coherence tomography (OCT) is a new intravascular imaging
technique with high resolution and could provide accurate morphological infor￾mation for plaques in coronary arteries. However, its segmentation is still com￾monly performed manually by experts which is time-consuming. The aim of
this study was to develop automatic techniques to characterize plaque components
and quantify plaque cap thickness using 3 machine learning methods including
convolutional neural network (CNN) with U-Net architecture, CNN with Fully
convolutional DenseNet (FC-DenseNet) architecture and support vector machine
(SVM). <i>In vivo</i> OCT and intravascular ultrasound (IVUS) images were acquired
from two patients at Emory University with informed consent obtained. Eighteen
OCT image slices which included lipid core and with acceptable image quality
were selected for our study. Manual segmentation from imaging experts was used
as the gold standard for model training and validation. Since OCT has limited
penetration, virtual histology IVUS was combined with OCT data to improve
reliability. A 3-fold cross-validation method was used for model training and vali￾dation. The overall tissue classification accuracy for the 18 slices studied (total
classification database sample size was 8580096 pixels) was 96.36% and
92.72% for U-Net and FC-DenseNet, respectively. The best average prediction
accuracy for lipid was 91.29% based on SVM, compared to 82.84% and
78.91% from U-Net and FC-DenseNet, respectively. The overall average accuracy
(Acc) differentiating lipid and fibrous tissue were 95.58%, 92.33% and 81.84%
for U-Net, FC-DenseNet and SVM, respectively. The average errors of U-Net,
FC-DenseNet and SVM from the 18 slices for cap thickness quantification were
8.83%, 10.71% and 15.85%. The average relative errors of minimum cap thick￾ness from 18 slices of U-Net, FC-DenseNet and SVM were 17.46%, 13.06%
This work is licensed under a Creative Commons Attribution 4.0 International License, which
permits unrestricted use, distribution, and reproduction in any medium, provided the original
work is properly cited.
Computer Modeling in Engineering & Sciences
DOI:10.32604/cmes.2020.09718
Article
Tech Science Press
and 22.20%, respectively. To conclude, CNN-based segmentation methods can
better characterize plaque compositions and quantify plaque cap thickness on
OCT images and are more likely to be used in the clinical arena. Large-scale studies are needed to further develop the methods and validate our findings.},
DOI = {10.32604/cmes.2020.09718}
}