<commit_before>.. 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.
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.


Getting started
---------------

Please see the `NiftyNet source code repository`_ for a detailed list of features and installation instructions.

Please follow the links below for the API documentation:

* :ref:`genindex`
* :ref:`modindex`


Citing NiftyNet
---------------

If you use NiftyNet, please cite the following paper:

::

  @InProceedings{niftynet17,
    author = {<NAME> and <NAME> and <NAME> and <NAME> and <NAME> and <NAME>},
    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`_.

.. _`TensorFlow`: https://www.tensorflow.org/<commit_msg>Issue #112: changed default genindex and modindex titles to more human-readable ones<commit_after>.. 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.
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.


Getting started
---------------

Please see the `NiftyNet source code repository`_ for a detailed list of features and installation instructions.

Please follow the links below for the API documentation:

* `Alphabetical index of the public API`_
* `List of all modules`_

.. _`Alphabetical index of the public API`: :ref:`genindex`
.. _`List of all modules`: :ref:`modindex`



Citing NiftyNet
---------------

If you use NiftyNet, please cite the following paper:

::

  @InProceedings{niftynet17,
    author = {<NAME> <NAME> Fidon, <NAME>, <NAME>, <NAME>, 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`_.

.. _`TensorFlow`: https://www.tensorflow.org/
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