NiftyNet¶
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. 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.
Getting started¶
Using NiftyNet’s 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
Please see the NiftyNet source code repository for a detailed list of features and installation instructions.
Examples¶
We are working to provide examples here showing how NiftyNet can be used and adapted to different image analysis problems. In the mean time please see the NiftyNet demos and network (re-)implementations.
API reference¶
Please see the Module Index.
Useful links¶
NiftyNet source code on CmicLab
NiftyNet source code mirror on GitHub
NiftyNet mailing list: nifty-net@live.ucl.ac.uk
Citing NiftyNet¶
If you use NiftyNet in your work, please cite 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
BibTeX entry:
@InProceedings{niftynet17,
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}
}
Licensing and copyright¶
Copyright 2017 University College London and the NiftyNet Contributors. NiftyNet is released under the Apache License, Version 2.0. Please see the LICENSE file in the NiftyNet source code repository for details.
Acknowledgements¶
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), University College London (UCL), the Science and Engineering South Consortium (SES), the STFC Rutherford-Appleton Laboratory, and NVIDIA.