Bone suppression on pediatric chest radiographs via a deep learning-based cascade model.

Journal: Computer methods and programs in biomedicine
PMID: 35032722

Abstract

BACKGROUND AND OBJECTIVE: Bone suppression images (BSIs) of chest radiographs (CXRs) have been proven to improve diagnosis of pulmonary diseases. To acquire BSIs, dual-energy subtraction (DES) or a deep-learning-based model trained with DES-based BSIs have been used. However, neither technique could be applied to pediatric patients owing to the harmful effects of DES. In this study, we developed a novel method for bone suppression in pediatric CXRs.

Authors

  • Kyungjin Cho
    Department of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
  • Jiyeon Seo
    Department of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
  • Sunggu Kyung
    Department of Biomedical Engineering, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, College of Medicine, University of Ulsan, Seoul, Republic of Korea.
  • Mingyu Kim
    Department of Medicine, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea.
  • Gil-Sun Hong
    Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine & Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu Seoul 05505, Republic of Korea. Electronic address: hgs2013@gmail.com.
  • Namkug Kim
    Department of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.

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