Deep Learning Algorithm for Automated Segmentation and Volume Measurement of the Liver and Spleen Using Portal Venous Phase Computed Tomography Images.

Journal: Korean journal of radiology
Published Date: Aug 1, 2020

Abstract

OBJECTIVE: Measurement of the liver and spleen volumes has clinical implications. Although computed tomography (CT) volumetry is considered to be the most reliable noninvasive method for liver and spleen volume measurement, it has limited application in clinical practice due to its time-consuming segmentation process. We aimed to develop and validate a deep learning algorithm (DLA) for fully automated liver and spleen segmentation using portal venous phase CT images in various liver conditions.

Authors

  • Yura Ahn
    Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
  • Jee Seok Yoon
    Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea.
  • Seung Soo Lee
    From the Department of Computer Science, Hanyang University, Seoul, Republic of Korea (K.J.C.); Department of Radiology and Research Institute of Radiology (J.K.J., S.S.L., Y.S.S., W.H.S., H.S.K., J.Y., J.H.K., S.Y.K.) and Department of Diagnostic Pathology (E.S.Y.), Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, South Korea; Department of Radiology, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea (J.Y.C.); Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Korea (Y.L.); and Department of Radiology, Hanyang University Medical Center, Hanyang University School of Medicine, Seoul, Korea (B.K.K.).
  • Heung Il Suk
    Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea.
  • Jung Hee Son
    Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
  • Yu Sub Sung
    From the Department of Computer Science, Hanyang University, Seoul, Republic of Korea (K.J.C.); Department of Radiology and Research Institute of Radiology (J.K.J., S.S.L., Y.S.S., W.H.S., H.S.K., J.Y., J.H.K., S.Y.K.) and Department of Diagnostic Pathology (E.S.Y.), Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, South Korea; Department of Radiology, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea (J.Y.C.); Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Korea (Y.L.); and Department of Radiology, Hanyang University Medical Center, Hanyang University School of Medicine, Seoul, Korea (B.K.K.).
  • Yedaun Lee
    From the Department of Computer Science, Hanyang University, Seoul, Republic of Korea (K.J.C.); Department of Radiology and Research Institute of Radiology (J.K.J., S.S.L., Y.S.S., W.H.S., H.S.K., J.Y., J.H.K., S.Y.K.) and Department of Diagnostic Pathology (E.S.Y.), Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, South Korea; Department of Radiology, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea (J.Y.C.); Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Korea (Y.L.); and Department of Radiology, Hanyang University Medical Center, Hanyang University School of Medicine, Seoul, Korea (B.K.K.).
  • Bo Kyeong Kang
    Department of Radiology, Hanyang University Medical Center, Hanyang University School of Medicine, Seoul, Korea.
  • Ho Sung Kim
    Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.

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