Automatic coronary artery segmentation and diagnosis of stenosis by deep learning based on computed tomographic coronary angiography.

Journal: European radiology
Published Date: Apr 8, 2022

Abstract

OBJECTIVES: Coronary computed tomography angiography (CCTA) has rapidly developed in the coronary artery disease (CAD) field. However, manual coronary artery tree segmentation and reconstruction are time-consuming and tedious. Deep learning algorithms have been successfully developed for medical image analysis to process extensive data. Thus, we aimed to develop a deep learning tool for automatic coronary artery reconstruction and an automated CAD diagnosis model based on a large, single-centre retrospective CCTA cohort.

Authors

  • Yiming Li
    Department of Cardiology, West China Hospital, Sichuan University, Chengdu 610041, China.
  • Yu Wu
    Key Laboratory of Pesticide and Chemical Biology of Ministry of Education, International Joint Research Center for Intelligent Biosensing Technology and Health, College of Chemistry, Central China Normal University, Wuhan, 430079, People's Republic of China.
  • Jingjing He
    Department of Cardiology, West China Hospital, Sichuan University, No. 37 Guoxue Street, Chengdu, 610041, People's Republic of China.
  • Weili Jiang
    College of New Energy and Materials, China University of Petroleum-Beijing Beijing 102249 China zhouguanglin2@163.com.
  • Jianyong Wang
    1 Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, Sichuan, P. R. China.
  • Yong Peng
    Department of Cardiology, West China Hospital, Sichuan University, Chengdu 610041, China.
  • Yuheng Jia
    Department of Cardiology, West China Hospital, Sichuan University, Chengdu 610041, China.
  • Tianyuan Xiong
    Department of Cardiology, West China Hospital, Sichuan University, Chengdu, China.
  • Kaiyu Jia
    Department of Cardiology, West China Hospital, Sichuan University, Chengdu 610041, China.
  • Zhang Yi
  • Mao Chen
    Department of Cardiology, West China Hospital, Sichuan University, Chengdu 610041, China.

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