A two-step automatic identification of contrast phases for abdominal CT images based on residual networks.

Journal: Insights into imaging
Published Date: Jun 27, 2025

Abstract

OBJECTIVES: To develop a deep learning model based on Residual Networks (ResNet) for the automated and accurate identification of contrast phases in abdominal CT images.

Authors

  • Qianhe Liu
    Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
  • Jiahui Jiang
    Hangzhou First People's Hospital, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China.
  • Kewei Wu
    Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
  • Yan Zhang
    Affiliated Hospital of Liaoning University of Traditional Chinese Medicine, Shenyang, 110032, China.
  • Nan Sun
    Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
  • Jiawen Luo
  • Te Ba
    Department of Radiology, The First Hospital of Fangshan District, Beijing, China.
  • Aiqing Lv
    Department of Radiology, Beijing Zhongguancun Hospital, Beijing, China.
  • Chuane Liu
    Wuhan branch, United Imaging Intelligence Co., Ltd., Wuhan, China.
  • Yiyu Yin
    Wuhan branch, United Imaging Intelligence Co., Ltd., Wuhan, China.
  • Zhenghan Yang
    Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
  • Hui Xu
    No 202 Hospital of People's Liberation Army, Liaoning 110003, China.

Keywords

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