A two-step automated quality assessment for liver MR images based on convolutional neural network.

Journal: European journal of radiology
Published Date: Jan 9, 2020

Abstract

PURPOSE: To propose an automatic approach based on a convolutional neural network (CNN) to evaluate the quality of T2-weighted liver magnetic resonance (MR) images as nondiagnostic (ND) or diagnostic (D).

Authors

  • Yida Wang
    Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China. Electronic address: ydwang@phy.ecnu.edu.cn.
  • Yang Song
    Biomedical and Multimedia Information Technology (BMIT) Research Group, School of IT, University of Sydney, NSW 2006, Australia. Electronic address: yson1723@uni.sydney.edu.au.
  • Fang Wang
    Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education and Hunan Key Laboratory for Computation and Simulation in Science and Engineering, Xiangtan University, Xiangtan, China.
  • Jingjing Sun
    School of Public Administration, Guangzhou University, Guangzhou, China.
  • Xinyi Gao
    Department of Radiology, Zhejiang Cancer Hospital, Hangzhou, China. Electronic address: 10301010213@fudan.edu.cn.
  • Zhe Han
    College of Plant Protection, Jilin Agricultural University, Changchun, P. R. China.
  • Lei Shi
  • Guoliang Shao
    Department of Radiology, Zhejiang Cancer Hospital, Hangzhou, China. Electronic address: shaogl@zjcc.org.cn.
  • Mingxia Fan
    Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China. Electronic address: mxfan@phy.ecnu.edu.cn.
  • Guang Yang
    National Heart and Lung Institute, Imperial College London, London, UK.

Keywords

External Resources

Popular Topics
Recent Journals