Opportunistic osteoporosis screening in multi-detector CT images using deep convolutional neural networks.

Journal: European radiology
Published Date: Oct 1, 2020

Abstract

OBJECTIVE: To explore the application of deep learning in patients with primary osteoporosis, and to develop a fully automatic method based on deep convolutional neural network (DCNN) for vertebral body segmentation and bone mineral density (BMD) calculation in CT images.

Authors

  • Yijie Fang
    Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, 519000, Guangdong Province, China.
  • Wei Li
    Department of Nephrology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
  • Xiaojun Chen
    Department of Gynecology, Obstetrics and Gynecology Hospital of Fudan University, Shanghai, China.
  • Keming Chen
    Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, 519000, Guangdong Province, China.
  • Han Kang
    Artificial Intelligence Scholar Center, Infervision, Beijing, 100025, China.
  • Pengxin Yu
    Institute of Advanced Research, Infervision, Beijing, China.
  • Rongguo Zhang
    Infervision, Beijing, China.
  • Jianwei Liao
    Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, 519000, Guangdong Province, China.
  • Guobin Hong
    Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, 519000, Guangdong Province, China. honggb@mail.sysu.edu.cn.
  • ShaoLin Li
    From the Department of Interventional Neuroradiology, Beijing Neurosurgical Institute and Beijing Tiantan Hospital of Capital Medical University, China (Q.L., P.J., Y.J., H.G., S.L., H.J., Y.L.).

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