A fully automatic artificial intelligence-based CT image analysis system for accurate detection, diagnosis, and quantitative severity evaluation of pulmonary tuberculosis.

Abstract

Authors

• Chenggong Yan
  The D-Lab, Dept of Precision Medicine, GROW - School for Oncology, Maastricht University Medical Center+, Maastricht, The Netherlands.
• Lingfeng Wang
  School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, China.
• Jie Lin
  Department of Reproductive Medicine, Zigong Hospital of Women and Children Health Care, Zigong, China.
• Jun Xu
  Department of Nephrology, The Affiliated Baiyun Hospital of Guizhou Medical University, Guizhou, China.
• Tianjing Zhang
  Philips Healthcare, Guangzhou, China.
• Jin Qi
  State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, P.R. China.
• Xiangying Li
  Department of Endocrinology, Endocrine Key Laboratory of Ministry of Health, PUMCH, CAMS & PUMC, 100730, Beijing, China.
• Wei Ni
  Data61, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Marsfield, NSW 2122, Australia.
• Guangyao Wu
  Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
• Jianbin Huang
  Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, No. 1838 Guangzhou Avenue North, Guangzhou, 510515, Guangdong, China.
• Yikai Xu
• Henry C Woodruff
  The D-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands.
• Philippe Lambin
  Department of Radiation Oncology (MAASTRO Clinic), Dr. Tanslaan 12, Maastricht, The Netherlands.

Keywords

External Resources