Deep learning network for medical volume data segmentation based on multi axial plane fusion.

Journal: Computer methods and programs in biomedicine
Published Date: Oct 22, 2021

Abstract

BACKGROUND AND OBJECTIVE: High-dimensional data generally contains more accurate information for medical image, e.g., computerized tomography (CT) data can depict the three dimensional structure of organs more precisely. However, the data in high-dimension often needs enormous computation and has high memory requirements in the deep learning convolution networks, while dimensional reduction usually leads to performance degradation.

Authors

  • Bo Huang
    Geriatrics Division, Department of Medicine, Peking University First Hospital, Beijing 100034, China.
  • Ziran Wei
    Changzheng Hospital, Shanghai, 200003, China.
  • Xianhua Tang
    Changzhou United Imaging Healthcare Surgical Technology Co.,Ltd, No.5 Longfan Road, Wujin High-Tech Industrial Development Zone, Changzhou, China.
  • Hamido Fujita
    Faculty of Software and Information Science, Iwate Prefectural University (IPU), Iwate 020-0693, Japan.
  • Qingping Cai
    Changzheng Hospital, Shanghai, 200003, China.
  • Yongbin Gao
    School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, 201620, China.
  • Tao Wu
    Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, Xinjiang, 830011, China.
  • Liang Zhou
    Department of Otorhinolaryngology, Eye & ENT Hospital, Fudan University, Shanghai, 200031, China. liang.zhou@fdeent.org.

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