Feasibility study of deep-learning-based bone suppression incorporated with single-energy material decomposition technique in chest X-rays.

Journal: The British journal of radiology
Published Date: Sep 19, 2022

Abstract

OBJECTIVE: To improve the detection of lung abnormalities in chest X-rays by accurately suppressing overlapping bone structures in the lung area. According to literature on missed lung cancer in chest X-rays, such structures are a significant cause of chest-related diagnostic errors.

Authors

  • Younghwan Lim
    Department of Radiation Convergence Engineering, Yonsei University, Wonju, Korea.
  • Minjae Lee
    Department of Radiation Convergence Engineering, Yonsei University, 1 Yonseidae-gil, Wonju 26493, Republic of Korea. Electronic address: yiminjae583@yonsei.ac.kr.
  • Hyosung Cho
    Department of Radiation Convergence Engineering, Yonsei University, Wonju, 26493, Korea.
  • Guna Kim
    Radiation Safety Management Division, Korea Atomic Energy Research Institute, Daejeon, Korea.
  • Jaegu Choi
    Electro-Medical Device Research Center, Korea Electrotechnology Research Institute, Ansan, Korea.
  • Bokyung Cha
    Electro-Medical Device Research Center, Korea Electrotechnology Research Institute, Ansan, Korea.
  • Sunkwon Kim
    Electro-Medical Device Research Center, Korea Electrotechnology Research Institute, Ansan, Korea.

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