NiftyNet: a deep-learning platform for medical imaging.

Journal: Computer methods and programs in biomedicine
Published Date: Jan 31, 2018

Abstract

BACKGROUND AND OBJECTIVES: Medical image analysis and computer-assisted intervention problems are increasingly being addressed with deep-learning-based solutions. Established deep-learning platforms are flexible but do not provide specific functionality for medical image analysis and adapting them for this domain of application requires substantial implementation effort. Consequently, there has been substantial duplication of effort and incompatible infrastructure developed across many research groups. This work presents the open-source NiftyNet platform for deep learning in medical imaging. The ambition of NiftyNet is to accelerate and simplify the development of these solutions, and to provide a common mechanism for disseminating research outputs for the community to use, adapt and build upon.

Authors

  • Eli Gibson
    Wellcome / EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, UK; Centre for Medical Image Computing (CMIC), Departments of Medical Physics & Biomedical Engineering and Computer Science, University College London, UK.
  • Wenqi Li
    Wellcome / EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, UK. Electronic address: wenqi.li@ucl.ac.uk.
  • Carole Sudre
    Centre for Medical Image Computing (CMIC), Departments of Medical Physics & Biomedical Engineering and Computer Science, University College London, UK.
  • Lucas Fidon
    Wellcome / EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, UK.
  • Dzhoshkun I Shakir
    Wellcome / EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, UK.
  • Guotai Wang
    Wellcome / EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, UK.
  • Zach Eaton-Rosen
    Centre for Medical Image Computing (CMIC), Departments of Medical Physics & Biomedical Engineering and Computer Science, University College London, UK.
  • Robert Gray
    Institute of Neurology, University College London, UK; National Hospital for Neurology and Neurosurgery, London, UK.
  • Tom Doel
    Wellcome / EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, UK.
  • Yipeng Hu
    Centre for Medical Image Computing (CMIC), Departments of Medical Physics & Biomedical Engineering and Computer Science, University College London, UK.
  • Tom Whyntie
    Centre for Medical Image Computing (CMIC), Departments of Medical Physics & Biomedical Engineering and Computer Science, University College London, UK.
  • Parashkev Nachev
    Institute of Neurology, University College London, London, WC1N 3BG, UK.
  • Marc Modat
    Centre for Medical Image Computing (CMIC), Departments of Medical Physics & Biomedical Engineering and Computer Science, University College London, UK.
  • Dean C Barratt
    Wellcome / EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, UK; Centre for Medical Image Computing (CMIC), Departments of Medical Physics & Biomedical Engineering and Computer Science, University College London, UK.
  • Sébastien Ourselin
    Wellcome / EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, UK.
  • M Jorge Cardoso
    Department of Biomedical EngineeringSchool of Biomedical Engineering and Imaging SciencesKing's College London WC2R 2LS London U.K.
  • Tom Vercauteren
    Wellcome / EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, UK.

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