Deep learning-based automatic sella turcica segmentation and morphology measurement in X-ray images.

Journal: BMC medical imaging
PMID: 36964517

Abstract

BACKGROUND: Although the morphological changes of sella turcica have been drawing increasing attention, the acquirement of linear parameters of sella turcica relies on manual measurement. Manual measurement is laborious, time-consuming, and may introduce subjective bias. This paper aims to develop and evaluate a deep learning-based model for automatic segmentation and measurement of sella turcica in cephalometric radiographs.

Authors

  • Qi Feng
    Panzhihua University, Panzhihua 617000, Sichuan, China.
  • Shu Liu
    Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen 518055, China. Electronic address: liushuhit@outlook.com.
  • Ju-Xiang Peng
    Department of Orthodontics, Guiyang Hospital of Stomatology, Guiyang, 550002, China.
  • Ting Yan
    Center for Biomedical Imaging, University of Science and Technology of China, Hefei, Anhui, China.
  • Hong Zhu
    Co-Innovation Center for the Sustainable Forestry in Southern China; Cerasus Research Center; College of Biology and the Environment, Nanjing Forestry University, Nanjing, China.
  • Zhi-Jun Zheng
    Department of Orthodontics, Guiyang Hospital of Stomatology, Guiyang, 550002, China.
  • Hong-Chao Feng
    College of Medicine, Guizhou University, Guiyang, 550025, China. hcfeng@gzu.edu.cn.

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