Automated Segmentation and Classification of Knee Synovitis Based on MRI Using Deep Learning.

Journal: Academic radiology
Published Date: Apr 1, 2024

Abstract

OBJECTIVES: To develop a deep learning (DL) model for segmentation of the suprapatellar capsule (SC) and infrapatellar fat pad (IPFP) based on sagittal proton density-weighted images and to distinguish between three common types of knee synovitis.

Authors

  • Qizheng Wang
    Department of Radiology, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, People's Republic of China.
  • Meiyi Yao
    Institute of Computing Technology, Chinese Academy of Sciences, Beijing, PR China (M.Y., X.S., S.J.).
  • Xinhang Song
    Institute of Computing Technology, Chinese Academy of Sciences, Beijing, PR China (M.Y., X.S., S.J.).
  • Yandong Liu
  • Xiaoying Xing
    Department of Radiology, Peking University Third Hospital, Haidian District, 49 North Garden Road, Beijing, 100191, People's Republic of China.
  • Yongye Chen
    Department of Radiology, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, People's Republic of China.
  • Fangbo Zhao
    Peking University, No.5 YiHeYuan Road, Haidian District, Beijing, PR China (F.Z.).
  • Ke Liu
    State Key Laboratory of Stress Cell Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian 361102, P.R. China.
  • Xiaoguang Cheng
    Department of Radiology, Beijing Jishuitan Hospital, Beijing, 100035, China.
  • Shuqiang Jiang
  • Ning Lang
    Department of Radiology, Peking University Third Hospital, Beijing 10019, China.

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