Development and Validation of a Deep Learning System for Segmentation of Abdominal Muscle and Fat on Computed Tomography.

Journal: Korean journal of radiology
Published Date:

Abstract

OBJECTIVE: We aimed to develop and validate a deep learning system for fully automated segmentation of abdominal muscle and fat areas on computed tomography (CT) images.

Authors

  • Hyo Jung Park
    Department of Radiology and Research Institute of Radiology, Asan Image Metrics, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
  • Yongbin Shin
    School of Computer Science and Engineering, Soongsil University, Seoul, Korea.
  • Jisuk Park
    Department of Radiology and Research Institute of Radiology, Asan Image Metrics, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
  • Hyosang Kim
    Department of Nephrology, Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
  • In Seob Lee
    Department of Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
  • Dong Woo Seo
    Department of Emergency Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
  • Jimi Huh
    Department of Radiology, Ajou University School of Medicine and Graduate School of Medicine, Ajou University Hospital, Suwon, Korea.
  • Tae Young Lee
    Department of Radiology, Ulsan University Hospital, Ulsan, Korea.
  • TaeYong Park
    School of Computer Science and Engineering, Soongsil University, Seoul, Korea.
  • Jeongjin Lee
    School of Computer Science and Engineering, Soongsil University, Seoul, Korea.
  • Kyung Won Kim
    Department of Pediatrics, Severance Children's Hospital, Institute of Allergy, Institute for Immunology and Immunological Diseases, Brain Korea 21 PLUS Project for Medical Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea. kwkim@yuhs.ac.