Automated detection and quantification of COVID-19 pneumonia: CT imaging analysis by a deep learning-based software.

Journal: European journal of nuclear medicine and molecular imaging
Published Date: Jul 14, 2020

Abstract

BACKGROUND: The novel coronavirus disease 2019 (COVID-19) is an emerging worldwide threat to public health. While chest computed tomography (CT) plays an indispensable role in its diagnosis, the quantification and localization of lesions cannot be accurately assessed manually. We employed deep learning-based software to aid in detection, localization and quantification of COVID-19 pneumonia.

Authors

  • Hai-Tao Zhang
    School of Artificial Intelligence and Automation, MOE Key Lab of Intelligent Control and Image Processing, Huazhong University of Science and Technology, Wuhan 430074, China.
  • Jin-Song Zhang
    Wuhan Huoshenshan Hospital, Wuhan, 430100, China.
  • Hai-Hua Zhang
    Department of Pulmonary and Critical Care Medicine, Tangdu Hospital, Air Force Military Medical University, Xi'an, 710038, China.
  • Yan-Dong Nan
    Department of Pulmonary and Critical Care Medicine, Tangdu Hospital, Air Force Military Medical University, Xi'an, 710038, China.
  • Ying Zhao
    Department of Pharmacy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • En-Qing Fu
    Department of Pulmonary and Critical Care Medicine, Tangdu Hospital, Air Force Military Medical University, Xi'an, 710038, China.
  • Yong-Hong Xie
    Department of Pulmonary and Critical Care Medicine, Tangdu Hospital, Air Force Military Medical University, Xi'an, 710038, China.
  • Wei Liu
    Department of Radiation Oncology, Mayo Clinic, Scottsdale, AZ, United States.
  • Wang-Ping Li
    Department of Pulmonary and Critical Care Medicine, Tangdu Hospital, Air Force Military Medical University, Xi'an, 710038, China.
  • Hong-Jun Zhang
    Department of Pulmonary and Critical Care Medicine, Tangdu Hospital, Air Force Military Medical University, Xi'an, 710038, China.
  • Hua Jiang
    Institute for Emergency and Disaster Medicine, Sichuan Academy of Medical Sciences, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, No. 32, Yi Huan Lu Xi Er Duan, Chengdu, Sichuan Province, China; Sino-Finnish Medical AI Research Center, No. 32, Yi Huan Lu Xi Er Duan, Chengdu, Sichuan Province, China. Electronic address: hua.jiang@traumabank.org.
  • Chun-Mei Li
    Department of Pulmonary and Critical Care Medicine, Tangdu Hospital, Air Force Military Medical University, Xi'an, 710038, China.
  • Yan-Yan Li
    Department of Pulmonary and Critical Care Medicine, Tangdu Hospital, Air Force Military Medical University, Xi'an, 710038, China.
  • Rui-Na Ma
    Department of Pulmonary and Critical Care Medicine, Tangdu Hospital, Air Force Military Medical University, Xi'an, 710038, China.
  • Shao-Kang Dang
    Department of Pulmonary and Critical Care Medicine, Tangdu Hospital, Air Force Military Medical University, Xi'an, 710038, China.
  • Bo-Bo Gao
    Department of Pulmonary and Critical Care Medicine, Tangdu Hospital, Air Force Military Medical University, Xi'an, 710038, China.
  • Xi-Jing Zhang
    Wuhan Huoshenshan Hospital, Wuhan, 430100, China. zhangxj918@163.com.
  • Tao Zhang
    Department of Traumatology, Chongqing Emergency Medical Center, Chongqing University Central Hospital, School of Medicine, Chongqing University, Chongqing, 40044, People's Republic of China.

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