Simple low-cost approaches to semantic segmentation in radiation therapy planning for prostate cancer using deep learning with non-contrast planning CT images.

Journal: Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Published Date:

Abstract

PURPOSE: Deep learning has shown great efficacy for semantic segmentation. However, there are difficulties in the collection, labeling and management of medical imaging data, because of ethical complications and the limited number of imaging studies available at a single facility. This study aimed to find a simple and low-cost method to increase the accuracy of deep learning semantic segmentation for radiation therapy of prostate cancer.

Authors

  • Takafumi Nemoto
    Department of Radiology, Keio University School of Medicine, Shinanomachi 35, Shinjuku-ku, Tokyo 160-8582, Japan; Division of Radiation Oncology, Saiseikai Yokohamashi Tobu Hospital, Shimosueyoshi 3-6-1, Tsurumi-ku, Yokohama-shi, Kanagawa 230-8765, Japan. Electronic address: takatohoku@gmail.com.
  • Natsumi Futakami
    Department of Radiation Oncology, Tokai University School of Medicine, Shimokasuya 143, Isehara-shi, Kanagawa 259-1143, Japan.
  • Masamichi Yagi
    HPC&AI Business Dept., Platform Technical Engineer Div., System Platform Solution Unit, Fujitsu Limited, World Trade Center Building, 4-1, Hamamatsucho 2-chome, Minato-ku, Tokyo 105-6125, Japan.
  • Etsuo Kunieda
    Department of Radiation Oncology, Tokai University School of Medicine, Shimokasuya 143, Isehara-shi, Kanagawa 259-1143, Japan.
  • Takeshi Akiba
    Department of Radiation Oncology, Tokai University School of Medicine, Shimokasuya 143, Isehara-shi, Kanagawa 259-1143, Japan.
  • Atsuya Takeda
    Radiation Oncology Center, Ofuna Chuo Hospital, Kamakura-shi 247-0056, Japan.
  • Naoyuki Shigematsu
    Department of Radiology, Keio University School of Medicine, Shinanomachi 35, Shinjuku-ku, Tokyo 160-8582, Japan.