Fully automated segmentation in temporal bone CT with neural network: a preliminary assessment study.

Journal: BMC medical imaging
Published Date: Nov 9, 2021

Abstract

BACKGROUND: Segmentation of important structures in temporal bone CT is the basis of image-guided otologic surgery. Manual segmentation of temporal bone CT is time- consuming and laborious. We assessed the feasibility and generalization ability of a proposed deep learning model for automated segmentation of critical structures in temporal bone CT scans.

Authors

  • Jiang Wang
    School of Electrical Engineering and Automation, Tianjin University, Tianjin, 300072, People's Republic of China.
  • Yi Lv
    Department of Hepatobiliary Surgery, First Affiliated Hospital; Xi'an Jiaotong University, P. R. China.
  • Junchen Wang
    School of Mechanical Engineering and AutomationBeihang UniversityBeijing100191China.
  • Furong Ma
  • Yali Du
    Department of Otorhinolaryngology-Head and Neck Surgery, Peking University Third Hospital, Peking University, NO. 49 North Garden Road, Haidian District, Beijing, 100191, China.
  • Xin Fan
    School of Software Technology, Dalian University of Technology, Dalian, 116024, China; Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, Dalian University of Technology, Dalian, 116024, China. Electronic address: xin.fan@ieee.org.
  • Menglin Wang
    Department of Otorhinolaryngology-Head and Neck Surgery, Peking University Third Hospital, Peking University, NO. 49 North Garden Road, Haidian District, Beijing, 100191, China.
  • Jia Ke
    Department of Colorectal Surgery, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.

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