Automated Identification of Optimal Portal Venous Phase Timing with Convolutional Neural Networks.

Journal: Academic radiology
Published Date:

Abstract

OBJECTIVES: To develop a deep learning-based algorithm to automatically identify optimal portal venous phase timing (PVP-timing) so that image analysis techniques can be accurately performed on post contrast studies.

Authors

  • Jingchen Ma
    School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China; Department of Radiology, Columbia University Medical Center, 630 West 168th Street, New York, NY 10032.
  • Laurent Dercle
    Department of Radiology, Columbia University Medical Center, 630 West 168th Street, New York, NY 10032; Gustave Roussy, Université Paris-Saclay, Université Paris-Saclay, Département D'imagerie Médicale, Villejuif, France.
  • Philip Lichtenstein
    Department of Radiology, Columbia University Medical Center, 630 West 168th Street, New York, NY 10032.
  • Deling Wang
    Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China.
  • Aiping Chen
    Department of Radiology, First Affiliated Hospital of NanJing Medical University, Nanjing, China.
  • Jianguo Zhu
    Department of Radiology, the Second Affiliated Hospital of Nanjing Medical University, Nanjing, China.
  • Hubert Piessevaux
    Cliniques Universitaires Saint-Luc, Brussels, Belgium.
  • Jun Zhao
  • Lawrence H Schwartz
    Department of Radiology, Columbia University College of Physicians and Surgeons, New York, NY, USA.
  • Lin Lu
    School of Economics and Management, Guangxi Normal University, Guilin, China.
  • Binsheng Zhao
    Department of Radiology, Columbia University Medical Center, 630 West 168th Street, New York, NY 10032.