Cardiac sarcoidosis classification with deep convolutional neural network-based features using polar maps.

Journal: Computers in biology and medicine
Published Date: Jan 1, 2019

Abstract

AIMS: The aim of this study was to determine whether deep convolutional neural network (DCNN)-based features can represent the difference between cardiac sarcoidosis (CS) and non-CS using polar maps.

Authors

  • Ren Togo
    Graduate School of Information Science and Technology, Hokkaido University, N-14, W-9, Kita-ku, Sapporo, Hokkaido, 060-0814, Japan.
  • Kenji Hirata
    Department of Diagnostic Imaging, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Japan.
  • Osamu Manabe
    Department of Nuclear Medicine, Hokkaido University Graduate School of Medicine, Hokkaido, 060-8638, Japan. Electronic address: osamumanabe817@med.hokudai.ac.jp.
  • Hiroshi Ohira
    Division of Respiratory Medicine, Department of Internal Medicine, Hokkaido University Graduate School of Medicine, Sapporo, Japan.
  • Ichizo Tsujino
    Division of Respiratory Medicine, Department of Internal Medicine, Hokkaido University Graduate School of Medicine, Sapporo, Japan.
  • Keiichi Magota
    Division of Medical Imaging and Technology, Hokkaido University Hospital, Hokkaido, 060-8638, Japan.
  • Takahiro Ogawa
    Faculty of Information Science and Technology, Hokkaido University, Sapporo, Japan.
  • Miki Haseyama
    Graduate School of Information Science and Technology, Hokkaido University, N-14, W-9, Kita-ku, Sapporo, Hokkaido, 060-0814, Japan.
  • Tohru Shiga
    Department of Nuclear Medicine, Hokkaido University Graduate School of Medicine, Hokkaido, 060-8638, Japan.

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