A generative flow-based model for volumetric data augmentation in 3D deep learning for computed tomographic colonography.

Journal: International journal of computer assisted radiology and surgery
Published Date:

Abstract

PURPOSE: Deep learning can be used for improving the performance of computer-aided detection (CADe) in various medical imaging tasks. However, in computed tomographic (CT) colonography, the performance is limited by the relatively small size and the variety of the available training datasets. Our purpose in this study was to develop and evaluate a flow-based generative model for performing 3D data augmentation of colorectal polyps for effective training of deep learning in CADe for CT colonography.

Authors

  • Tomoki Uemura
    3D Imaging Research, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 25 New Chardon Street, Suite 400C, Boston, MA, 02114, USA.
  • Janne J Näppi
    From the 3D Imaging Research Lab, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 25 New Chardon St, Suite 400C, Boston, MA 02114 (R.T., J.J.N., N.K., T.H., H.Y.); Department of Information Science and Technology, National Institute of Technology, Oshima College, Yamaguchi, Japan (R.T.); Department of Medical Physics and Engineering, Graduate School of Medicine, Osaka University, Suita, Osaka, Japan (J.O.); Department of Medical Physics, University of Applied Sciences Giessen, Giessen, Germany (N.K.); Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea (S.H.K.); Department of Surgical Sciences, University of Torino, Turin, Italy (D.R.); and Candiolo Cancer Institute, Fondazione del Piemonte per l'Oncologia-Istituto di Ricovero e Cura a Carattere Scientifico (FPO-IRCCS), Candiolo, Turin, Italy (D.R.).
  • Yasuji Ryu
    Department of Radiology, Tonami General Hospital, 1-61 Shintomi-cho, Tonami, Toyama, 939-1395, Japan.
  • Chinatsu Watari
    3D Imaging Research, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 25 New Chardon Street, Suite 400C, Boston, MA, 02114, USA.
  • Tohru Kamiya
    Department of Mechanical and Control Engineering, Kyushu Institute of Technology, 1-1 Sensui-cho, Kitakyushu, 804-8550, Japan.
  • Hiroyuki Yoshida
    From the 3D Imaging Research Lab, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 25 New Chardon St, Suite 400C, Boston, MA 02114 (R.T., J.J.N., N.K., T.H., H.Y.); Department of Information Science and Technology, National Institute of Technology, Oshima College, Yamaguchi, Japan (R.T.); Department of Medical Physics and Engineering, Graduate School of Medicine, Osaka University, Suita, Osaka, Japan (J.O.); Department of Medical Physics, University of Applied Sciences Giessen, Giessen, Germany (N.K.); Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea (S.H.K.); Department of Surgical Sciences, University of Torino, Turin, Italy (D.R.); and Candiolo Cancer Institute, Fondazione del Piemonte per l'Oncologia-Istituto di Ricovero e Cura a Carattere Scientifico (FPO-IRCCS), Candiolo, Turin, Italy (D.R.).