Identification of high-risk population of pneumoconiosis using deep learning segmentation of lung 3D images and radiomics texture analysis.

Journal: Computer methods and programs in biomedicine
Published Date:

Abstract

OBJECTION: The aim of this study is to develop an early-warning model for identifying high-risk populations of pneumoconiosis by combining lung 3D images and radiomics lung texture features.

Authors

  • Yafeng Liu
    Information Engineering University, Lanzhou 730050, China.
  • Jing Wu
    School of Pharmaceutical Science, Jiangnan University, Wuxi, 214122, Jiangsu, China.
  • Jiawei Zhou
    State Key Laboratory of Oral Diseases, West China School of Stomatology, West China Hospital of Stomatology, Department of Orthodontics, Sichuan University, People's Republic of China.
  • Jianqiang Guo
    School of Physical Science and Technology, Southwest Jiaotong University, Chengdu 622731, China.
  • Chao Liang
    School of Life Sciences, Zhengzhou University Zhengzhou 450001 Henan China pingaw@126.com.
  • Yingru Xing
    School of Medicine, Anhui University of Science and Technology, Huainan, PR China; Department of Clinical Laboratory, Anhui Zhongke Gengjiu Hospital, Hefei, PR China.
  • Zhongyu Wang
    a Key Laboratory of Industrial Ecology and Environmental Engineering (MOE), School of Environmental Science and Technology , Dalian University of Technology , Dalian , China.
  • Lijuan Chen
    State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center, Chengdu, Sichuan 610041, PR China. Electronic address: chenlijuan125@163.com.
  • Yan Ding
    Department of Computer Science and Engineering, Changshu Institute of Technology, Changshu 215500, China.
  • Dingfei Ren
    Occupational Control Hospital of Huaihe Energy Group, Huainan, PR China. Electronic address: happyrdf@163.com.
  • Ying Bai
  • Dong Hu
    School of Medicine, Anhui University of Science and Technology, Huainan, PR China; Anhui Province Engineering Laboratory of Occupational Health and Safety, Anhui University of Science and Technology, Huainan, PR China; Key Laboratory of Industrial Dust Prevention and Control & Occupational Safety and Health of the Ministry of Education, Anhui University of Science and Technology, Huainan, PR China. Electronic address: austhudong@126.com.