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 GitHub, 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 and 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.


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 GitHub 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 Centre for Medical Engineering (CME), the School of Biomedical Engineering and Imaging Sciences at King's College London (BMEIS) and the High-dimensional Imaging Group (HIG) at the UCL Institute of Neurology.


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), King's College London (KCL), the Science and Engineering South Consortium (SES), the STFC Rutherford-Appleton Laboratory, and NVIDIA.

© The NiftyNet Consortium 2019