An open source convolutional neural networks platform for medical image analysis and image-guided therapy

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What is NiftyNet?

NiftyNet is a TensorFlow-based open-source convolutional neural networks (CNNs) platform for research in medical image analysis and image-guided therapy. NiftyNet’s modular structure is designed for sharing networks and pre-trained models. Using this modular structure you can:

  • Get started with established pre-trained networks using built-in tools;
  • Adapt existing networks to your imaging data;
  • Quickly build new solutions to your own image analysis problems.

The code is available via GitLab, or you can quickly get started with the PyPI module available here.


NiftyNet currently supports medical image segmentation and generative adversarial networks. NiftyNet is not intended for clinical use. Other features of NiftyNet include:

Easy-to-customise interfaces of network components

Sharing networks and pre-trained models

Support for 2-D, 2.5-D, 3-D, 4-D inputs

Efficient discriminative training with multiple-GPU support

Implementation of recent networks (HighRes3DNet, 3D U-net, V-net, DeepMedic)

Comprehensive evaluation metrics for medical image segmentation


If you use NiftyNet in your work, please cite Gibson, Li et al. 2017. The NiftyNet platform originated in software developed for Li et. al. 2017. Please click below for the full citations and BibTeX entries.

  • Gibson, Li et. al. 2017

    E. Gibson*, W. Li*, C. Sudre, L. Fidon, D. Shakir, G. Wang, Z. Eaton-Rosen, R. Gray, T. Doel, Y. Hu, T. Whyntie, P. Nachev, D. C. Barratt, S. Ourselin, M. J. Cardoso and T. Vercauteren (2017) NiftyNet: a deep-learning platform for medical imaging. arXiv (preprint) 1709.03485

    author = {Eli Gibson and Wenqi Li and Carole Sudre and Lucas Fidon and Dzoshkun Shakir and Guotai Wang and Zach Eaton-Rosen and Robert Gray and Tom Doel and Yipeng Hu and Tom Whyntie and Parashkev Nachev and Dean C. Barratt and Sebastien Ourselin and M. Jorge Cardoso and Tom Vercauteren},
    title = {NiftyNet: a deep-learning platform for medical imaging},
    year = {2017},
    eprint = {1709.03485},
    eprintclass = {cs.CV},
    eprinttype = {arXiv},
  • Li et. al. 2017

    Li W.*, Wang G., Fidon L., Ourselin S., Cardoso M.J., Vercauteren T. (2017) On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext Task. In: Niethammer M. et al. (eds) Information Processing in Medical Imaging. IPMI 2017. Lecture Notes in Computer Science, vol 10265. Springer, Cham. DOI: 10.1007/978-3-319-59050-9_28

    author = {Li, Wenqi and Wang, Guotai and Fidon, Lucas and Ourselin, Sebastien and Cardoso, M. Jorge and Vercauteren, Tom},
    title = {On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext Task},
    booktitle = {International Conference on Information Processing in Medical Imaging (IPMI)},
    year = {2017}


A number of models from the literature have been (re)implemented in the NiftyNet framework. These are listed below. All networks can be applied in 2D, 2.5D and 3D configurations and are reimplemented from their original presentation with their default parameters.

Further details can be found in the GitLab networks section here.

Loss Functions

Publications relating to the various loss functions used in the NiftyNet framework can be found listed below.


NiftyNet is released under the Apache License, Version 2.0. Please see the LICENSE file in the NiftyNet source code repository for details.

The NiftyNet Consortium

NiftyNet is a consortium of research groups, including the Wellcome EPSRC Centre for Interventional and Surgical Sciences (WEISS), UCL's Centre for Medical Image Computing (CMIC) and High-dimensional Imaging Group (HIG). WEISS acts as the consortium lead.


This project is grateful for the support from the Wellcome Trust, the Engineering and Physical Sciences Research Council (EPSRC), the National Institute for Health Research (NIHR), the Department of Health (DoH), Cancer Research UK (CRUK), the NIHR UCLH/UCL Biomedical Research Centre (BRC), University College London (UCL), the Science and Engineering South Consortium (SES), the STFC Rutherford-Appleton Laboratory, and NVIDIA.

© The NiftyNet Consortium 2017