.. NiftyNet documentation master file, created by sphinx-quickstart on Wed Aug 30 14:13:50 2017. You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. 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`_. .. _`NiftyNet demos`: https://cmiclab.cs.ucl.ac.uk/CMIC/NiftyNet/tree/dev/demos .. _`network (re-)implementations`: https://cmiclab.cs.ucl.ac.uk/CMIC/NiftyNet/tree/dev/niftynet/network API reference ^^^^^^^^^^^^^ Please see the :ref:`modindex`. Useful links ^^^^^^^^^^^^ :ref:`genindex` :ref:`search` `NiftyNet website`_ `NiftyNet source code on CmicLab`_ `NiftyNet source code mirror on GitHub`_ NiftyNet mailing list: nifty-net@live.ucl.ac.uk .. _`NiftyNet website`: http://niftynet.io/ .. _`NiftyNet source code on CmicLab`: https://cmiclab.cs.ucl.ac.uk/CMIC/NiftyNet .. _`NiftyNet source code mirror on GitHub`: https://github.com/NifTK/NiftyNet 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: .. code-block:: bibtex @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} } .. _`Li et. al. 2017`: http://doi.org/10.1007/978-3-319-59050-9_28 .. _`On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext Task.`: http://doi.org/10.1007/978-3-319-59050-9_28 .. _`10.1007/978-3-319-59050-9_28`: http://doi.org/10.1007/978-3-319-59050-9_28 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`_. .. _`TensorFlow`: https://www.tensorflow.org/ .. _`Wellcome EPSRC Centre for Interventional and Surgical Sciences`: http://www.ucl.ac.uk/weiss .. _`NiftyNet source code repository`: https://cmiclab.cs.ucl.ac.uk/CMIC/NiftyNet .. _`Centre for Medical Image Computing`: http://cmic.cs.ucl.ac.uk/ .. _`Centre for Medical Image Computing (CMIC)`: http://cmic.cs.ucl.ac.uk/ .. _`University College London (UCL)`: http://www.ucl.ac.uk/ .. _`Wellcome Trust`: https://wellcome.ac.uk/ .. _`Engineering and Physical Sciences Research Council (EPSRC)`: https://www.epsrc.ac.uk/ .. _`National Institute for Health Research (NIHR)`: https://www.nihr.ac.uk/ .. _`Department of Health (DoH)`: https://www.gov.uk/government/organisations/department-of-health .. _`Science and Engineering South Consortium (SES)`: https://www.ses.ac.uk/ .. _`STFC Rutherford-Appleton Laboratory`: http://www.stfc.ac.uk/about-us/where-we-work/rutherford-appleton-laboratory/ .. _`NVIDIA`: http://www.nvidia.com .. toctree:: :maxdepth: 4 :caption: Contents: