Deep Learning-based Image Cytometry Using a Bit-pattern Kernel-filtering Algorithm to Avoid Multi-counted Cell Determination.

Journal: Anticancer research
Published Date: Aug 1, 2023

Abstract

BACKGROUND/AIM: In pathology, the digitization of tissue slide images and the development of image analysis by deep learning have dramatically increased the amount of information obtainable from tissue slides. This advancement is anticipated to not only aid in pathological diagnosis, but also to enhance patient management. Deep learning-based image cytometry (DL-IC) is a technique that plays a pivotal role in this process, enabling cell identification and counting with precision. Accurate cell determination is essential when using this technique. Herein, we aimed to evaluate the performance of our DL-IC in cell identification.

Authors

  • Tomoki Abe
    Division of Gastrointestinal Surgery, Department of Surgery, Graduate School of Medicine, Kobe University, Kobe, Japan.
  • Kimihiro Yamashita
    Division of Gastrointestinal Surgery, Department of Surgery, Graduate School of Medicine, Kobe University, Kobe, Hyogo, Japan.
  • Toru Nagasaka
    Department of Pathology, Chubu Rosai Hospital, Japan Organization of Occupational Health and Safety, Nagoya, Japan.
  • Mitsugu Fujita
    Department of Microbiology, Kindai University Faculty of Medicine, Osakasayama, Osaka 589-8511, Japan.
  • Kyousuke Agawa
    Division of Gastrointestinal Surgery, Department of Surgery, Graduate School of Medicine, Kobe University, Kobe, Japan.
  • Masayuki Ando
    Division of Gastrointestinal Surgery, Department of Surgery, Graduate School of Medicine, Kobe University, Kobe, Japan.
  • Tomosuke Mukoyama
    Division of Gastrointestinal Surgery, Department of Surgery, Graduate School of Medicine, Kobe University, Kobe, Japan.
  • Kota Yamada
    Division of Gastrointestinal Surgery, Department of Surgery, Graduate School of Medicine, Kobe University, Kobe, Japan.
  • Souichiro Miyake
    Division of Hepato-Biliary and Pancreatic Surgery, Department of Surgery, Graduate School of Medicine, Kobe University, Kobe, Japan.
  • Masafumi Saito
    Department of Disaster and Emergency and Critical Care Medicine, Graduate School of Medicine, Kobe University, Kobe, Japan.
  • Ryuichiro Sawada
    Division of Gastrointestinal Surgery, Department of Surgery, Kobe University Graduate School of Medicine, Kobe, Japan.
  • Hiroshi Hasegawa
    Laboratory of Hygienic Sciences, Kobe Pharmaceutical University, 4-19-1 Motoyamakitamachi, Higasinada-ku, Kobe, Hyogo, 658-8558, Japan.
  • Takeru Matsuda
  • Takashi Kato
    Division of Gastrointestinal Surgery, Department of Surgery, Graduate School of Medicine, Kobe University, Kobe, Japan.
  • Hitoshi Harada
    Division of Gastrointestinal Surgery, Department of Surgery, Kobe University Graduate School of Medicine, Kobe, Japan.
  • Naoki Urakawa
    Division of Gastrointestinal Surgery, Department of Surgery, Graduate School of Medicine, Kobe University, Kobe, Hyogo, Japan.
  • Hironobu Goto
    Division of Gastrointestinal Surgery, Department of Surgery, Graduate School of Medicine, Kobe University, Kobe, Hyogo, Japan.
  • Shingo Kanaji
    Division of Gastrointestinal Surgery, Department of Surgery, Graduate School of Medicine, Kobe University, Kobe, Japan. kanashin@med.kobe-u.ac.jp.
  • Hiroaki Yanagimoto
    Division of Hepato-Biliary and Pancreatic Surgery, Department of Surgery, Graduate School of Medicine, Kobe University, Kobe, Japan.
  • Taro Oshikiri
  • Tetsuo Ajiki
  • Takumi Fukumoto
    Division of Hepato-Biliary-Pancreatic Surgery Department of Surgery Kobe University Graduate School of Medicine Kobe Japan.
  • Yoshihiro Kakeji
    Division of Gastrointestinal Surgery, Department of Surgery, Graduate School of Medicine, Kobe University, Kobe, Japan. kakeji@med.kobe-u.ac.jp.

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