Comparison of semi-automatic and deep learning-based automatic methods for liver segmentation in living liver transplant donors.

Journal: Diagnostic and interventional radiology (Ankara, Turkey)
Published Date: Jan 1, 2020

Abstract

PURPOSE: To compare the accuracy and repeatability of emerging machine learning based (i.e. deep) automatic segmentation algorithms with those of well-established semi-automatic (interactive) methods for determining liver volume in living liver transplant donors at computerized tomography (CT) imaging.

Authors

  • A Emre Kavur
    Graduate School of Natural and Applied Sciences, Dokuz Eylül University, İzmir, Turkey.
  • Naciye Sinem Gezer
    Department of Radiology, Dokuz Eylül University School of Medicine, İzmir, Turkey.
  • Mustafa Barış
    Department of Radiology, Dokuz Eylül University School of Medicine, İzmir, Turkey.
  • Yusuf Şahin
    Department of Computer Engineering, İstanbul Technical University, İstanbul, Turkey.
  • Savaş Özkan
    Department of Electrical and Electronics Engineering, Middle East Technical University, Ankara, Turkey.
  • Bora Baydar
    Department of Electrical and Electronics Engineering, Middle East Technical University, Ankara, Turkey.
  • Ulaş Yüksel
    Graduate School of Natural and Applied Sciences, Dokuz Eylül University, İzmir, Turkey.
  • Çağlar Kılıkçıer
    Department of Computer Engineering, Uludağ University, Bursa, Turkey.
  • Şahin Olut
    Department of Computer Engineering, İstanbul Technical University, İstanbul, Turkey.
  • Gözde Bozdağı Akar
    Department of Electrical and Electronics Engineering, Middle East Technical University, Ankara, Turkey.
  • Gözde Ünal
    Istanbul Technical University, Department of Computer Engineering, Maslak, Sarıyer, Istanbul, Turkey.
  • Oğuz Dicle
    Department of Radiology, Dokuz Eylül University School of Medicine, İzmir, Turkey.
  • M Alper Selver
    Department of Electrical and Electronics Engineering, Dokuz Eylül University, İzmir, Turkey.

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