Automatic cervical lymphadenopathy segmentation from CT data using deep learning.

Journal: Diagnostic and interventional imaging
Published Date: May 19, 2021

Abstract

PURPOSE: The purpose of this study was to develop a fast and automatic algorithm to detect and segment lymphadenopathy from head and neck computed tomography (CT) examination.

Authors

  • Adele Courot
    General Electric Healthcare, 78530 Buc, France. Electronic address: adele.courot@ge.com.
  • Diana L F Cabrera
    General Electric Healthcare, 78530 Buc, France; Université de Reims Champagne Ardenne, CReSTIC EA 3804, 51097 Reims, France.
  • Nicolas Gogin
    General Electric Healthcare, 78530 Buc, France.
  • Loic Gaillandre
    Centre Libéral d'Imagerie Médicale de l'Agglomération Lilloise, 59000 Lille, France.
  • Geoffrey Rico
    Hôpital La Conception, 13000 Marseille, France.
  • Jules Zhang-Yin
    HôpitalTenon, APHP, 75020 Paris, France.
  • Mickael Elhaik
    Institut Gustave Roussy, 94800 Villejuif, France.
  • François Bidault
    Department of Radiology, Institut Gustave Roussy, 94800 Villejuif, France; Laboratoire d'Imagerie Biomédicale Multimodale Paris-Saclay. BIOMAPS, UMR 1281. Université Paris-Saclay, Inserm, CNRS, CEA, 94800 Villejuif, France.
  • Imad Bousaid
    Direction de la Transformation Numérique et des Systèmes d'Information, Gustave Roussy, Université Paris-Saclay, 94805, Villejuif, France.
  • Nathalie Lassau
    Laboratoire d'Imagerie Biomédicale Multimodale Paris-Saclay, BIOMAPS, UMR 1281, Université Paris-Saclay, Inserm, CNRS, CEA, 94800 Villejuif, France; Department of Imaging, Institut Gustave Roussy, Université Paris-Saclay. 94800 Villejuif, France.

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