Quantifying the calcification of abdominal aorta and major side branches with deep learning.

Journal: Clinical radiology
PMID:

Abstract

AIM: To explore the possibility of a neural network-based method for quantifying calcifications of the abdominal aorta and its branches.

Authors

  • J Halkoaho
    Department of Radiology, HUS Diagnostic Center, Helsinki University Hospital and University of Helsinki, Helsinki, Finland; Department of Physics, University of Helsinki, P.O. Box 64, FI-00014 Helsinki, Finland. Electronic address: Johannes.halkoaho@helsinki.fi.
  • O Niiranen
    Department of Surgery, University of Turku, Turku, Finland; Department of Surgery, Seinäjoki Central Hospital, Seinäjoki, Finland.
  • E Salli
    Department of Radiology, HUS Diagnostic Center, Helsinki University Hospital and University of Helsinki, Helsinki, Finland.
  • T Kaseva
    Department of Radiology, HUS Diagnostic Center, Helsinki University Hospital and University of Helsinki, Helsinki, Finland.
  • S Savolainen
    Department of Radiology, HUS Diagnostic Center, Helsinki University Hospital and University of Helsinki, Helsinki, Finland; Department of Physics, University of Helsinki, P.O. Box 64, FI-00014 Helsinki, Finland.
  • M Kangasniemi
    Department of Radiology, HUS Diagnostic Center, Helsinki University Hospital and University of Helsinki, Helsinki, Finland.
  • H Hakovirta
    Department of Surgery, University of Turku, Turku, Finland; Division of Gastroenterology and Urology, Turku University Hospital, Turku, Finland; Department of Surgery, Satasairaala, Pori, Finland.