Abnormal lung quantification in chest CT images of COVID-19 patients with deep learning and its application to severity prediction.

Journal: Medical physics
Published Date: Mar 9, 2021

Abstract

OBJECTIVE: Computed tomography (CT) provides rich diagnosis and severity information of COVID-19 in clinical practice. However, there is no computerized tool to automatically delineate COVID-19 infection regions in chest CT scans for quantitative assessment in advanced applications such as severity prediction. The aim of this study was to develop a deep learning (DL)-based method for automatic segmentation and quantification of infection regions as well as the entire lungs from chest CT scans.

Authors

  • Fei Shan
    Gastrointestinal Cancer Center, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China.
  • Yaozong Gao
  • Jun Wang
    Department of Speech, Language, and Hearing Sciences and the Department of Neurology, The University of Texas at Austin, Austin, TX 78712, USA.
  • Weiya Shi
  • Nannan Shi
    Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, 201508, China.
  • Miaofei Han
    Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China.
  • Zhong Xue
    Shanghai United Imaging Intelligence Co Ltd., Shanghai, China.
  • Dinggang Shen
    School of Biomedical Engineering, ShanghaiTech University, Shanghai, China.
  • Yuxin Shi
    Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China. shiyx828288@163.com.

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