Automated classification of increased uptake regions in bone single-photon emission computed tomography/computed tomography images using three-dimensional deep convolutional neural network.

Journal: Nuclear medicine communications
Published Date: Aug 1, 2021

Abstract

OBJECTIVE: This study proposes an automated classification of benign and malignant in highly integrated regions in bone single-photon emission computed tomography/computed tomography (SPECT/CT) using a three-dimensional deep convolutional neural network (3D-DCNN).

Authors

  • Masakazu Tsujimoto
    Fujita Health University Hospital, 1-98 Dengakugakubo, Kutsukake cho, Toyoake City, Aichi, 470-1192, Japan.
  • Atsushi Teramoto
    Faculty of Radiological Technology, School of Health Sciences, Fujita Health University, 1-98 Dengakugakubo, Kutsukake, Toyoake, Aichi 470-1192, Japan.
  • Masakazu Dosho
    School of Medical Sciences.
  • Shingo Tanahashi
    Department of Radiology, Fujita Health University Hospital.
  • Ayami Fukushima
    Department of Radiology, Fujita Health University Hospital.
  • Seiichiro Ota
    Department of Radiology, School of Medicine, Fujita Health University, Toyoake, Japan.
  • Yoshitaka Inui
    Department of Radiology, School of Medicine, Fujita Health University, Toyoake, Japan.
  • Ryo Matsukiyo
    Department of Radiology, Fujita Health University School of Medicine, 1-98, Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan.
  • Yuuki Obama
    Department of Radiology, School of Medicine, Fujita Health University, Toyoake, Japan.
  • Hiroshi Toyama
    School of Medicine, Fujita Health University, 1-98 Dengakugakubo, Kutsukake cho, Toyoake City, Aichi, 470-1192, Japan.

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