Comparison of atlas-based and deep learning methods for organs at risk delineation on head-and-neck CT images using an automated treatment planning system.

Journal: Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Published Date: Nov 1, 2022

Abstract

BACKGROUND AND PURPOSE: To investigate the performance of head-and-neck (HN) organs-at-risk (OAR) automatic segmentation (AS) using four atlas-based (ABAS) and two deep learning (DL) solutions.

Authors

  • Madalina Costea
    Centre Léon Bérard, 28 rue Laennec, 69373 LYON Cedex 08, France; CREATIS, CNRS UMR5220, Inserm U1044, INSA-Lyon, Université Lyon 1, Villeurbanne, France.
  • Alexandra Zlate
    MedEuropa, Strada Turnului 8, Brașov 500152, Romania.
  • Morgane Durand
    Centre Léon Bérard, 28 rue Laennec, 69373 LYON Cedex 08, France.
  • Thomas Baudier
    Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, Lyon, France.
  • Vincent Gregoire
    Department of Radiation Oncology, Centre Leon Berard, Lyon, France.
  • David Sarrut
    Université de Lyon, CREATIS, CNRS UMR5220, Inserm U1294, INSA-Lyon, Université Lyon 1, Lyon, France.
  • Marie-Claude Biston
    Centre Léon Bérard, 28 rue Laennec, 69373 LYON Cedex 08, France; CREATIS, CNRS UMR5220, Inserm U1044, INSA-Lyon, Université Lyon 1, Villeurbanne, France. Electronic address: marie-claude.biston@lyon.unicancer.fr.

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