Fully automatic, multiorgan segmentation in normal whole body magnetic resonance imaging (MRI), using classification forests (CFs), convolutional neural networks (CNNs), and a multi-atlas (MA) approach.

Journal: Medical physics
Published Date: Aug 31, 2017

Abstract

PURPOSE: As part of a program to implement automatic lesion detection methods for whole body magnetic resonance imaging (MRI) in oncology, we have developed, evaluated, and compared three algorithms for fully automatic, multiorgan segmentation in healthy volunteers.

Authors

  • Ioannis Lavdas
    Imperial College Comprehensive Cancer Imaging Centre (C.C.I.C.), Hammersmith Campus, Commonwealth Building Main Office, Ground Floor, Du Cane Road, London, W12 0NN, UK.
  • Ben Glocker
    Kheiron Medical Technologies, London, UK.
  • Konstantinos Kamnitsas
    Biomedical Image Analysis Group, Imperial College London, UK. Electronic address: konstantinos.kamnitsas12@imperial.ac.uk.
  • Daniel Rueckert
    Biomedical Image Analysis (BioMedIA) Group, Department of Computing, Imperial College London, UK. Electronic address: d.rueckert@imperial.ac.uk.
  • Henrietta Mair
    Department of Imaging, University College London Hospitals NHS Foundation Trust, Euston Road, London, NW1 2BU, UK.
  • Amandeep Sandhu
    Department of Radiology Hammersmith Hospital, Imperial College Healthcare NHS Trust, DuCane Road, London, W12 0NN, UK.
  • Stuart A Taylor
    Department of Imaging, University College London Hospitals NHS Foundation Trust, Euston Road, London, NW1 2BU, UK.
  • Eric O Aboagye
    Imperial College Comprehensive Cancer Imaging Centre (C.C.I.C.), Hammersmith Campus, Commonwealth Building Main Office, Ground Floor, Du Cane Road, London, W12 0NN, UK.
  • Andrea G Rockall
    Department of Radiology, The Royal Marsden NHS Foundation Trust, Fulham Road, London, SW3 6JJ, UK.

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