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An automatic multi-class coronary atherosclerosis plaque detection and classification framework

  • Fengjun Zhao
  • Bin Wu
  • Fei Chen
  • Xin Cao
  • Huangjian Yi
  • Yuqing Hou
  • Xiaowei He
  • Jimin Liang
Original Article

Abstract

Detection of different classes of atherosclerotic plaques is important for early intervention of coronary artery diseases. However, previous methods focused either on the detection of a specific class of coronary plaques or on the distinction between plaques and normal arteries, neglecting the classification of different classes of plaques. Therefore, we proposed an automatic multi-class coronary atherosclerosis plaque detection and classification framework. Firstly, we retrieved the transverse cross sections along centerlines from the computed tomography angiography. Secondly, we extracted the region of interests based on coarse segmentation. Thirdly, we extracted a random radius symmetry (RRS) feature vector, which incorporates multiple descriptions into a random strategy and greatly augments the training data. Finally, we fed the RRS feature vector into the multi-class coronary plaque classifier. In experiments, we compared our proposed framework with other methods on the cross sections of Rotterdam Coronary Datasets, including 729 non-calcified plaques, 511 calcified plaques, and 546 mixed plaques. Our RRS with support vector machine outperforms the intensity feature vector and the random forest classifier, with the average precision of 92.6 ± 1.9% and average recall of 94.3 ± 2.1%. The proposed framework provides a computer-aided diagnostic method for multi-class plaque detection and classification.

Graphical abstract

Diagram of the proposed automatic multi-class coronary atherosclerosis plaque detection and classification framework.

Keywords

Coronary atherosclerosis plaque Detection Classification Computed tomography angiography 

Notes

Acknowledgements

The authors also would like to thank Dr. Muhan Liu for his assistance in polishing the manuscript.

Funding information

This work was partly supported by the National Key R&D Program of China under Grant No. 2016YFC1300300; the National Natural Science Foundation of China under Grant Nos. 61601363, 61701403, 61401264, 11571012, 81530058; the Natural Science Research Plan Program in Shaanxi Province of China under Grant Nos. 2017JQ6017, 2017JQ6006, 2015JM6322, and 2015JZ019; and the Scientific Research Foundation of Northwest University.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All the CTA data are obtained from public database. No human/animal experiments are involved in this paper.

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Copyright information

© International Federation for Medical and Biological Engineering 2018

Authors and Affiliations

  1. 1.School of Information Sciences and TechnologyNorthwest UniversityXi’anChina
  2. 2.Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and TechnologyXidian UniversityXi’anChina

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