Deep Learning Based Automatic Segmentation of the Thoracic Aorta from Chest Computed Tomography in Healthy Korean Adults.

Journal: European journal of vascular and endovascular surgery : the official journal of the European Society for Vascular Surgery
PMID: 39089448

Abstract

OBJECTIVE: Segmenting the aorta into zones based on anatomical landmarks is a current trend to better understand interventions for aortic dissection or aneurysm. However, comprehensive reference values for aortic zones are lacking. The aim of this study was to establish reference values for aortic size using a fully automated deep learning based segmentation method.

Authors

  • Hyun Jung Koo
    Department of Radiology, University of Ulsan College of Medicine, Seoul, Republic of Korea.
  • June-Goo Lee
    Biomedical Engineering Research Center, University of Ulsan College of Medicine, Asan Medical Center, Seoul 05505, Korea.
  • Jung-Bok Lee
    Clinical Epidemiology and Biostatistics, Asan Medical Centre, University of Ulsan College of Medicine, Seoul, Republic of Korea.
  • Joon-Won Kang
    Department of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.
  • Dong Hyun Yang
    Department of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.

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