Automated Magnetic Resonance Image Segmentation of Spinal Structures at the L4-5 Level with Deep Learning: 3D Reconstruction of Lumbar Intervertebral Foramen.

Journal: Orthopaedic surgery
Published Date: Aug 18, 2022

Abstract

OBJECTIVE: 3D reconstruction of lumbar intervertebral foramen (LIVF) has been beneficial in evaluating surgical trajectory. Still, the current methods of reconstructing the 3D LIVF model are mainly based on manual segmentation, which is laborious and time-consuming. This study aims to explore the feasibility of automatically segmenting lumbar spinal structures and increasing the speed and accuracy of 3D lumbar intervertebral foramen (LIVF) reconstruction on magnetic resonance image (MRI) at the L4-5 level.

Authors

  • Tao Chen
    School of Automation, Northwestern Polytechnical University, Xi'an, 710072, Shaanxi, China.
  • Zhi-Hai Su
    Department of Spinal Surgery, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China.
  • Zheng Liu
    ICSC World Laboratory, Geneva, Switzerland.
  • Min Wang
    National and Local Joint Engineering Research Center of Ecological Treatment Technology for Urban Water Pollution, Wenzhou University, Wenzhou 325035, China.
  • Zhi-Fei Cui
    Department of Spinal Surgery, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China.
  • Lei Zhao
    Brain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming, China.
  • Lian-Jun Yang
    Department of Spinal Surgery, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China.
  • Wei-Cong Zhang
    Department of Spinal Surgery, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China.
  • Xiang Liu
    College of Agricultural Science and Engineering, Hohai University, Nanjing 210098, China; Anhui Provincial Key Laboratory of Environmental Pollution Control and Resource Reuse, Anhui Jianzhu University, Hefei 230009, China.
  • Jin Liu
    School of Computer Science and Engineering, Central South University, Changsha, China.
  • Shu-Yuan Tan
    School of Biomedical Engineering, Sun Yat-sen University, Shenzhen, China.
  • Shao-Lin Li
    Department of Radiology, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China.
  • Qian-Jin Feng
    School of Biomedical Engineering, Southern Medical University, Guangdong Provincial Key Laboratory of Medical Image Processing, Guangzhou, China.
  • Shu-Mao Pang
    School of Biomedical Engineering, Guangzhou Medical University, Guangzhou, China.
  • Hai Lu
    School of Information Science and Engineering, Xiamen University, Xiamen 361005, China. luhai@xmu.edu.cn.

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