Automatic segmentation of thoracic CT images using three deep learning models.

Journal: Cancer radiotherapie : journal de la Societe francaise de radiotherapie oncologique
Published Date: Jul 5, 2022

Abstract

PURPOSE: Deep learning (DL) techniques are widely used in medical imaging and in particular for segmentation. Indeed, manual segmentation of organs at risk (OARs) is time-consuming and suffers from inter- and intra-observer segmentation variability. Image segmentation using DL has given very promising results. In this work, we present and compare the results of segmentation of OARs and a clinical target volume (CTV) in thoracic CT images using three DL models.

Authors

  • D M Khalal
    Department of Physics, Faculty of Sciences, Laboratory of dosing, analysis and characterization in high resolution, Ferhat Abbas Sétif 1 University, El Baz campus, 19137 Sétif, Algeria. Electronic address: doreamaria.khalal@univ-setif.dz.
  • A Behouch
    Department of Physics, Faculty of Sciences, Laboratory of dosing, analysis and characterization in high resolution, Ferhat Abbas Sétif 1 University, El Baz campus, 19137 Sétif, Algeria.
  • H Azizi
    Department of Physics, Faculty of Sciences, Laboratory of dosing, analysis and characterization in high resolution, Ferhat Abbas Sétif 1 University, El Baz campus, 19137 Sétif, Algeria.
  • N Maalej
    Department of Physics, Khalifa University, Abu Dhabi, United Arab Emirates.

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