Automatic detection of acute ischemic stroke using non-contrast computed tomography and two-stage deep learning model.

Journal: Computer methods and programs in biomedicine
Published Date: Aug 15, 2020

Abstract

BACKGROUND AND OBJECTIVE: Currently, it is challenging to detect acute ischemic stroke (AIS)-related changes on computed tomography (CT) images. Therefore, we aimed to develop and evaluate an automatic AIS detection system involving a two-stage deep learning model.

Authors

  • Mizuho Nishio
    Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan.
  • Sho Koyasu
    Research Center for Advanced Science and Technology, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8904, Japan; Department of Diagnostic Radiology, Ichinomiya Nishi Hospital, 1-Hira Kaimei, Ichinomiya, Aichi 494-0001, Japan.
  • Shunjiro Noguchi
    Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan. Electronic address: noguchi.shunjiro.c95@kyoto-u.jp.
  • Takao Kiguchi
    Department of Diagnostic Radiology, Ichinomiya Nishi Hospital, 1-Hira Kaimei, Ichinomiya, Aichi 494-0001, Japan.
  • Kanako Nakatsu
    Department of Radiology, Osaka Red Cross Hospital, 5-30 Fudegasakicho, Tennoji-ku, Osaka 543-8555, Japan.
  • Thai Akasaka
    Department of Radiology, Osaka Red Cross Hospital, 5-30 Fudegasakicho, Tennoji-ku, Osaka 543-8555, Japan.
  • Hiroki Yamada
    Department of Diagnostic Radiology, Ichinomiya Nishi Hospital, 1-Hira Kaimei, Ichinomiya, Aichi 494-0001, Japan.
  • Kyo Itoh
    Department of Radiology, Osaka Red Cross Hospital, 5-30 Fudegasakicho, Tennoji-ku, Osaka 543-8555, Japan.

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