Multimodal image translation via deep learning inference model trained in video domain.

Journal: BMC medical imaging
Published Date: Jul 14, 2022

Abstract

BACKGROUND: Current medical image translation is implemented in the image domain. Considering the medical image acquisition is essentially a temporally continuous process, we attempt to develop a novel image translation framework via deep learning trained in video domain for generating synthesized computed tomography (CT) images from cone-beam computed tomography (CBCT) images.

Authors

  • Jiawei Fan
    Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.
  • Zhiqiang Liu
    Shenzhen Key Laboratory of Reproductive Immunology for Peri-implantation, Shenzhen Zhongshan Institute for Reproductive Medicine and Genetics, Shenzhen, China.
  • Dong Yang
    College of Chemistry and Chemical Engineering, Shaanxi University of Science and Technology Xi'an 710021 China yangdong@sust.edu.cn.
  • Jian Qiao
    Advanced Materials Division, Key Laboratory of Multifunctional Nanomaterials and Smart Systems, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou 215123, China.
  • Jun Zhao
  • Jiazhou Wang
    Department of radiation oncology, Fudan University Shanghai Cancer Center, Shanghai, China.
  • Weigang Hu
    Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.

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