Development of automatic measurement for patellar height based on deep learning and knee radiographs.

Journal: European radiology
Published Date: Sep 1, 2020

Abstract

OBJECTIVES: To develop and evaluate the performance of a deep learning-based system for automatic patellar height measurements using knee radiographs.

Authors

  • Qin Ye
    Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital of Hangzhou Medical College, Hangzhou, China.
  • Qiang Shen
    Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Wei Yang
    Key Laboratory of Structure-Based Drug Design and Discovery (Shenyang Pharmaceutical University), Ministry of Education, School of Traditional Chinese Materia Medica, Shenyang Pharmaceutical University, Wenhua Road 103, Shenyang 110016, PR China. Electronic address: 421063202@qq.com.
  • Shuai Huang
    Department of Industrial and Systems Engineering, University of Washington, Seattle, WA 98195 USA.
  • Zhiqiang Jiang
    Hangzhou Jianpei Technology Co., Ltd, Hangzhou, China.
  • Linyang He
    Hangzhou Jianpei Technology Co., Ltd, Hangzhou, China.
  • Xiangyang Gong
    Rehabilitation Medicine Center, Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou 310014, Zhejiang, China.; Institute of Artificial Intelligence and Remote Imaging, Hangzhou Medical College, Hangzhou 310014, China. Electronic address: gong.xy@vip.163.com.

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