Developing and verifying automatic detection of active pulmonary tuberculosis from multi-slice spiral CT images based on deep learning.

Journal: Journal of X-ray science and technology
Published Date: Jan 1, 2020

Abstract

OBJECTIVE: Diagnosis of tuberculosis (TB) in multi-slice spiral computed tomography (CT) images is a difficult task in many TB prevalent locations in which experienced radiologists are lacking. To address this difficulty, we develop an automated detection system based on artificial intelligence (AI) in this study to simplify the diagnostic process of active tuberculosis (ATB) and improve the diagnostic accuracy using CT images.

Authors

  • Luyao Ma
    CT-MRI Room, Affiliated Hospital of Hebei University, Baoding, Hebei, China.
  • Yun Wang
    Department of Anesthesiology, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, People's Republic of China.
  • Lin Guo
    Shenzhen Zhiying Medical Imaging, Shenzhen, Guangdong, China.
  • Yu Zhang
    College of Marine Electrical Engineering, Dalian Maritime University, Dalian, China.
  • Ping Wang
    School of Chemistry and Chemical Engineering, Shandong University of Technology, 255049, Zibo, PR China. Electronic address: wangping876@163.com.
  • Xu Pei
    CT-MRI Room, Affiliated Hospital of Hebei University, Baoding, Hebei, China.
  • Lingjun Qian
    Shenzhen Zhiying Medical Imaging, Shenzhen, Guangdong, China.
  • Stefan Jaeger
    Lister Hill National Center for Biomedical Communications, National Library of Medicine, Bethesda, Maryland, United States.
  • Xiaowen Ke
    Shenzhen Zhiying Medical Imaging, Shenzhen, Guangdong, China.
  • Xiaoping Yin
    CT-MRI Room, Affiliated Hospital of Hebei University, Baoding, Hebei, China.
  • Fleming Y M Lure
    Shenzhen Zhiying Medical Imaging, Shenzhen, Guangdong, China.

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