Development of in-house fully residual deep convolutional neural network-based segmentation software for the male pelvic CT.

Journal: Radiation oncology (London, England)
Published Date: Jul 22, 2021

Abstract

BACKGROUND: This study aimed to (1) develop a fully residual deep convolutional neural network (CNN)-based segmentation software for computed tomography image segmentation of the male pelvic region and (2) demonstrate its efficiency in the male pelvic region.

Authors

  • Hideaki Hirashima
    Department of Radiation Oncology and Image-applied Therapy, Graduate School of Medicine, Kyoto University, Kyoto, 606-8507, Japan.
  • Mitsuhiro Nakamura
    Department of Radiation Oncology and Image-Applied Therapy, Kyoto University, Japan.
  • Pascal Baillehache
    Rist, Inc., Impact HUB Tokyo, 2-11-3 Meguro, Meguro-ku, Tokyo, 153-0063, Japan.
  • Yusuke Fujimoto
    Rist, Inc., Impact HUB Tokyo, 2-11-3 Meguro, Meguro-ku, Tokyo, 153-0063, Japan.
  • Shota Nakagawa
    Rist, Inc., Impact HUB Tokyo, 2-11-3 Meguro, Meguro-ku, Tokyo, 153-0063, Japan.
  • Yusuke Saruya
    Rist, Inc., Impact HUB Tokyo, 2-11-3 Meguro, Meguro-ku, Tokyo, 153-0063, Japan.
  • Tatsumasa Kabasawa
    Rist, Inc., Impact HUB Tokyo, 2-11-3 Meguro, Meguro-ku, Tokyo, 153-0063, Japan.
  • Takashi Mizowaki
    Department of Radiation Oncology and Image-applied Therapy, Graduate School of Medicine, Kyoto University, Kyoto, 606-8507, Japan.

Keywords

External Resources

Popular Topics
Recent Journals