Automatic segmentation of kidneys in computed tomography images using U-Net.

Journal: Cancer radiotherapie : journal de la Societe francaise de radiotherapie oncologique
Published Date: Feb 2, 2023

Abstract

PURPOSE: Accurate segmentation of target volumes and organs at risk from computed tomography (CT) images is essential for treatment planning in radiation therapy. The segmentation task is often done manually making it time-consuming. Besides, it is biased to the clinician experience and subject to inter-observer variability. Therefore, and due to the development of artificial intelligence tools and particularly deep learning (DL) algorithms, automatic segmentation has been proposed as an alternative. The purpose of this work is to use a DL-based method to segment the kidneys on CT images for radiotherapy treatment planning.

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.
  • 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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