Automatic segmentation model of intercondylar fossa based on deep learning: a novel and effective assessment method for the notch volume.

Journal: BMC musculoskeletal disorders
Published Date: May 6, 2022

Abstract

BACKGROUND: Notch volume is associated with anterior cruciate ligament (ACL) injury. Manual tracking of intercondylar notch on MR images is time-consuming and laborious. Deep learning has become a powerful tool for processing medical images. This study aims to develop an MRI segmentation model of intercondylar fossa based on deep learning to automatically measure notch volume, and explore its correlation with ACL injury.

Authors

  • Mifang Li
    Department of Medical Imaging, The Third Affiliated Hospital, Southern Medical University, Guangzhou, China.
  • Hanhua Bai
    Southern Medical University, 1838 shatai Road, Baiyun District, Guangzhou, 510515, Guangdong province, China.
  • Feiyuan Zhang
    Department of Medical Imaging, The Third Affiliated Hospital, Southern Medical University, 183 Zhongshan Avenue West, Tianhe District, Guangzhou, 510630, Guangdong province, China.
  • Yujia Zhou
    Department of Biomedical Informatics and Data Science, School of Medicine, Yale University, New Haven, CT 06510, United States.
  • Qiuyu Lin
    Nuclear Medicine Department, The First Hospital of Jilin University, Changchun 130000, Jilin, China.
  • Quan Zhou
    Department of Medical Laboratory, General Hospital of Southern Theater of PLA, Guangzhou 51010, China.
  • Qianjin Feng
    Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China. Electronic address: qianjinfeng08@gmail.com.
  • Lingyan Zhang
    Department of Medical Imaging, The Third Affiliated Hospital, Southern Medical University, Guangzhou, China.

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