Object and anatomical feature recognition in surgical video images based on a convolutional neural network.

Journal: International journal of computer assisted radiology and surgery
Published Date: Jun 24, 2021

Abstract

PURPOSE: Artificial intelligence-enabled techniques can process large amounts of surgical data and may be utilized for clinical decision support to recognize or forecast adverse events in an actual intraoperative scenario. To develop an image-guided navigation technology that will help in surgical education, we explored the performance of a convolutional neural network (CNN)-based computer vision system in detecting intraoperative objects.

Authors

  • Yoshiko Bamba
    Department of Surgery, Institute of Gastroenterology, Tokyo Women's Medical University, 8-1, Kawadacho Shinjuku-ku, Tokyo, 162-8666, Japan. bamba.yoshiko@twmu.ac.jp.
  • Shimpei Ogawa
    Department of Surgery, Institute of Gastroenterology, Tokyo Women's Medical University, 8-1, Kawadacho Shinjuku-ku, Tokyo, 162-8666, Japan.
  • Michio Itabashi
    Department of Surgery, Institute of Gastroenterology, Tokyo Women's Medical University, 8-1, Kawadacho Shinjuku-ku, Tokyo, 162-8666, Japan.
  • Hironari Shindo
    Otsuki Municipal Central Hospital, Yamanashi, Japan.
  • Shingo Kameoka
    Ushiku Aiwa Hospital, Ibaraki, Japan.
  • Takahiro Okamoto
    Department of Breast Endocrinology Surgery, Tokyo Women's Medical University, Tokyo, Japan.
  • Masakazu Yamamoto
    Department of Surgery, Institute of Gastroenterology, Tokyo Women's Medical University, Tokyo, Japan.

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