Development and Validation of a Modified Three-Dimensional U-Net Deep-Learning Model for Automated Detection of Lung Nodules on Chest CT Images From the Lung Image Database Consortium and Japanese Datasets.

Journal: Academic radiology
Published Date: Aug 21, 2020

Abstract

RATIONALE AND OBJECTIVES: A more accurate lung nodule detection algorithm is needed. We developed a modified three-dimensional (3D) U-net deep-learning model for the automated detection of lung nodules on chest CT images. The purpose of this study was to evaluate the accuracy of the developed modified 3D U-net deep-learning model.

Authors

  • Kazuhiro Suzuki
    Department of Radiology, Juntendo University Faculty of Medicine and Graduate School of Medicine, 3-1-3, Hongo, Bunkyo-ku, Tokyo 113-8431, Japan. Electronic address: ka-suzz@juntendo.ac.jp.
  • Yujiro Otsuka
    Department of Radiology, Juntendo University School of Medicine.
  • Yukihiro Nomura
    The University of Tokyo Hospital.
  • Kanako K Kumamaru
    From the Applied Imaging Science Laboratory, Department of Radiology, Brigham and Women's Hospital, 75 Francis St, Boston, MA 02115 (T.C., A.A.G., K.K.K., F.J.R., D.M.); Harvard T.H. Chan School of Public Health, Boston, Mass (S.Y.); and Department of Radiology, Brigham and Women's Hospital, Boston, Mass (T.K., B.R.).
  • Ryohei Kuwatsuru
    Department of Radiology, Juntendo University Faculty of Medicine and Graduate School of Medicine, 3-1-3, Hongo, Bunkyo-ku, Tokyo 113-8431, Japan.
  • Shigeki Aoki

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