Polycystic liver: automatic segmentation using deep learning on CT is faster and as accurate compared to manual segmentation.

Journal: European radiology
Published Date: Feb 10, 2022

Abstract

OBJECTIVE: This study aimed to develop and investigate the performance of a deep learning model based on a convolutional neural network (CNN) for the automatic segmentation of polycystic livers at CT imaging.

Authors

  • Bénédicte Cayot
    Department of Medical Imaging, Hospices Civils de Lyon, University of Lyon, Lyon, France. benedicte.cayot@chu-lyon.fr.
  • Laurent Milot
    Body and VIR Radiology Department, Hospices Civils de Lyon, Hôpital Edouard Herriot, Lyon, France.
  • Olivier Nempont
    Philips Research France, 92150 Suresnes, France.
  • Anna S Vlachomitrou
    Service d'imagerie médicale et interventionnelle, Hôpital Edouard Herriot, 5 Place d'Arsonval, 69003, Lyon, France.
  • Carole Langlois-Jacques
    Service d'imagerie médicale et interventionnelle, Hôpital Edouard Herriot, 5 Place d'Arsonval, 69003, Lyon, France.
  • Jérôme Dumortier
    Service d'imagerie médicale et interventionnelle, Hôpital Edouard Herriot, 5 Place d'Arsonval, 69003, Lyon, France.
  • Olivier Boillot
    Service d'imagerie médicale et interventionnelle, Hôpital Edouard Herriot, 5 Place d'Arsonval, 69003, Lyon, France.
  • Karine Arnaud
    Service d'imagerie médicale et interventionnelle, Hôpital Edouard Herriot, 5 Place d'Arsonval, 69003, Lyon, France.
  • Thijs R M Barten
    Service d'imagerie médicale et interventionnelle, Hôpital Edouard Herriot, 5 Place d'Arsonval, 69003, Lyon, France.
  • Joost P H Drenth
    Service d'imagerie médicale et interventionnelle, Hôpital Edouard Herriot, 5 Place d'Arsonval, 69003, Lyon, France.
  • Pierre-Jean Valette
    Service d'imagerie médicale et interventionnelle, Hôpital Edouard Herriot, 5 Place d'Arsonval, 69003, Lyon, France.

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