Coarse-to-fine airway segmentation using multi information fusion network and CNN-based region growing.

Journal: Computer methods and programs in biomedicine
Published Date: Jan 8, 2022

Abstract

BACKGROUND AND OBJECTIVES: Automatic airway segmentation from chest computed tomography (CT) scans plays an important role in pulmonary disease diagnosis and computer-assisted therapy. However, low contrast at peripheral branches and complex tree-like structures remain as two mainly challenges for airway segmentation. Recent research has illustrated that deep learning methods perform well in segmentation tasks. Motivated by these works, a coarse-to-fine segmentation framework is proposed to obtain a complete airway tree.

Authors

  • Jinquan Guo
    School of Mechanical engineering and Automation, Fuzhou University, Fuzhou 350108, China.
  • Rongda Fu
    School of Mechanical engineering and Automation, Fuzhou University, Fuzhou 350108, China.
  • Lin Pan
    School of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China.
  • Shaohua Zheng
    School of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China.
  • Liqin Huang
    School of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China.
  • Bin Zheng
    School of Electrical and Computer Engineering, University of Oklahoma, 101 David L. Boren Blvd, Norman, OK, 73019, USA.
  • Bingwei He
    School of Mechanical engineering and Automation, Fuzhou University, Fuzhou 350108, China. Electronic address: mebwhe@fzu.edu.cn.

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