Deep Learning-Based Image Conversion Improves the Reproducibility of Computed Tomography Radiomics Features: A Phantom Study.

Journal: Investigative radiology
PMID: 34839305

Abstract

OBJECTIVES: This study aimed to evaluate the usefulness of deep learning-based image conversion to improve the reproducibility of computed tomography (CT) radiomics features.

Authors

  • Seul Bi Lee
    Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.
  • Yeon Jin Cho
  • Youngtaek Hong
    CONNECT-AI R&D Center, Yonsei University College of Medicine.
  • Dawun Jeong
  • Jina Lee
    Microbiology and Functionality Research Group, World Institute of Kimchi, Gwangju 61755, Republic of Korea.
  • Soo-Hyun Kim
    Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.
  • Seunghyun Lee
    Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA. Electronic address: seunghyun.lee.22@gmail.com.
  • Young Hun Choi
    Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 110-744, Korea. iater@snu.ac.kr.

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