Comparison of the Accuracy of a Deep Learning Method for Lesion Detection in PET/CT and PET/MRI Images.

Journal: Molecular imaging and biology
Published Date:

Abstract

PURPOSE: Develop a universal lesion recognition algorithm for PET/CT and PET/MRI, validate it, and explore factors affecting performance.

Authors

  • Lifang Pang
    Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, No. 180, Fenglin Road, Shanghai, 200032, People's Republic of China.
  • Zheng Zhang
    Key Laboratory of Sustainable and Development of Marine Fisheries, Ministry of Agriculture and Rural Affairs, Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao, PR China.
  • Guobing Liu
    Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, No. 180, Fenglin Road, Shanghai, 200032, People's Republic of China.
  • Pengcheng Hu
    Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, No. 180, Fenglin Road, Shanghai, 200032, People's Republic of China.
  • Shuguang Chen
    Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, No. 180, Fenglin Road, Shanghai, 200032, People's Republic of China.
  • Yushen Gu
    Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, No. 180, Fenglin Road, Shanghai, 200032, People's Republic of China.
  • Yukun Huang
    College of Information Science and Engineering, Northeastern University, China.
  • Jia Zhang
    Department of Geriatrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China.
  • Yuhang Shi
    Shanghai United Imaging Healthcare Co., Ltd., Shanghai, 201807, China.
  • Tuoyu Cao
    Shanghai United Imaging Healthcare Co., Ltd., Shanghai, 201807, China.
  • Yiqiu Zhang
    Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, No. 180, Fenglin Road, Shanghai, 200032, People's Republic of China. zhang.yiqiu@zs-hospital.sh.cn.
  • Hongcheng Shi
    Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, Shanghai, China.