Deep learning-based auto segmentation using generative adversarial network on magnetic resonance images obtained for head and neck cancer patients.

Journal: Journal of applied clinical medical physics
Published Date: Mar 9, 2022

Abstract

PURPOSE: Adaptive radiotherapy requires auto-segmentation in patients with head and neck (HN) cancer. In the current study, we propose an auto-segmentation model using a generative adversarial network (GAN) on magnetic resonance (MR) images of HN cancer for MR-guided radiotherapy (MRgRT).

Authors

  • Daisuke Kawahara
    Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan.
  • Masato Tsuneda
    Department of Radiation Oncology, MR Linac ART Division, Graduate School of Medicine, Chiba University, Chiba, 260-8670, Japan.
  • Shuichi Ozawa
    Hiroshima High-Precision Radiotherapy Cancer Center, Hiroshima, Japan; Department of Radiation Oncology, Institute of Biomedical & Health Science, Hiroshima University, Hiroshima, Japan.
  • Hiroyuki Okamoto
    Radiation Safety and Quality Assurance Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, Japan.
  • Mitsuhiro Nakamura
    Department of Radiation Oncology and Image-Applied Therapy, Kyoto University, Japan.
  • Teiji Nishio
    Medical Physics Laboratory, Division of Health Science, Graduate School of Medicine, Osaka University, Osaka, Japan.
  • Yasushi Nagata
    Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan.

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