NiftyNet is a TensorFlow-based open-source convolutional neural networks (CNN) 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
NiftyNet is a consortium of research groups (WEISS -- Wellcome EPSRC Centre for Interventional and Surgical Sciences, CMIC -- Centre for Medical Image Computing, HIG -- High-dimensional Imaging Group), where WEISS acts as the consortium lead.
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 pretrained 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
*2.5-D: volumetric images processed as a stack of 2D slices; 4-D: co-registered multi-modal 3D volumes
NiftyNet release notes are available here.
- Please install the appropriate TensorFlow package*:
pip install tensorflow-gpu==1.7for TensorFlow with GPU supportpip install tensorflow==1.7for CPU-only TensorFlow
pip install niftynet
*All other NiftyNet dependencies are installed automatically as part of the pip installation process.
*To install from the source repository, please checkout the instructions.
The API reference and how-to guides are available on Read the Docs.
NiftyNet source code on CmicLab
NiftyNet source code mirror on GitHub
NiftyNet mailing list: nifty-net@live.ucl.ac.uk
Stack Overflow for general questions
If you use NiftyNet in your work, please cite Gibson and Li, et al. 2018:
- E. Gibson*, W. Li*, C. Sudre, L. Fidon, D. I. Shakir, G. Wang, Z. Eaton-Rosen, R. Gray, T. Doel, Y. Hu, T. Whyntie, P. Nachev, M. Modat, D. C. Barratt, S. Ourselin, M. J. Cardoso^ and T. Vercauteren^ (2018) NiftyNet: a deep-learning platform for medical imaging, Computer Methods and Programs in Biomedicine. DOI: 10.1016/j.cmpb.2018.01.025
BibTeX entry:
@article{Gibson2018,
title = "NiftyNet: a deep-learning platform for medical imaging",
journal = "Computer Methods and Programs in Biomedicine",
year = "2018",
issn = "0169-2607",
doi = "https://doi.org/10.1016/j.cmpb.2018.01.025",
url = "https://www.sciencedirect.com/science/article/pii/S0169260717311823",
author = "Eli Gibson and Wenqi Li and Carole Sudre and Lucas Fidon and
Dzhoshkun I. Shakir and Guotai Wang and Zach Eaton-Rosen and
Robert Gray and Tom Doel and Yipeng Hu and Tom Whyntie and
Parashkev Nachev and Marc Modat and Dean C. Barratt and
Sébastien Ourselin and M. Jorge Cardoso and Tom Vercauteren",
}
The NiftyNet platform originated in software developed for 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
Copyright 2018 University College London and the NiftyNet Contributors. NiftyNet is released under the Apache License, Version 2.0. Please see the LICENSE file for details.
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, University College London (UCL), the Science and Engineering South Consortium (SES), the STFC Rutherford-Appleton Laboratory, and NVIDIA.