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"Development Status :: 3 - Alpha",
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"Intended Audience :: Healthcare Industry",
"Intended Audience :: Science/Research",
"License :: OSI Approved :: Apache Software License",
"Programming Language :: Python",
"Programming Language :: Python :: 2",
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"Topic :: Scientific/Engineering :: Image Recognition",
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"description": "# NiftyNet\n\n![](\"https://github.com/NifTK/NiftyNet/raw/dev/niftynet-logo.png\")
\n\n[](https://github.com/NifTK/NiftyNet/commits/dev)\n[](https://github.com/NifTK/NiftyNet)\n[](https://github.com/NifTK/NiftyNet/blob/dev/LICENSE)\n[](https://badge.fury.io/py/NiftyNet)\n\nNiftyNet is a [TensorFlow][tf]-based open-source convolutional neural networks (CNN) platform for research in medical image analysis and image-guided therapy.\nNiftyNet's modular structure is designed for sharing networks and pre-trained models.\nUsing this modular structure you can:\n\n* Get started with established pre-trained networks using built-in tools\n* Adapt existing networks to your imaging data\n* Quickly build new solutions to your own image analysis problems\n\nNiftyNet is a consortium of research organisations\n(BMEIS -- [School of Biomedical Engineering and Imaging Sciences, King's College London][bmeis];\nWEISS -- [Wellcome EPSRC Centre for Interventional and Surgical Sciences, UCL][weiss];\nCMIC -- [Centre for Medical Image Computing, UCL][cmic];\nHIG -- High-dimensional Imaging Group, UCL), where BMEIS acts as the consortium lead.\n\n\n### Features\n\n* Easy-to-customise interfaces of network components\n* Sharing networks and pretrained models\n* Support for 2-D, 2.5-D, 3-D, 4-D inputs*\n* Efficient training with multiple-GPU support\n* Implementation of recent networks (HighRes3DNet, 3D U-net, V-net, DeepMedic)\n* Comprehensive evaluation metrics for medical image segmentation\n\n NiftyNet is not intended for clinical use.\n\n NiftyNet release notes are available [here][changelog].\n\n *2.5-D: volumetric images processed as a stack of 2D slices;\n4-D: co-registered multi-modal 3D volumes\n\n[changelog]: CHANGELOG.md\n\n\n### Installation\n\n1. Please install the appropriate [TensorFlow][tf] package*:\n * [`pip install \"tensorflow-gpu>=1.13.2, <=1.14\"`][tf-pypi-gpu] for TensorFlow with GPU support\n * [`pip install \"tensorflow>=1.13.2, <=1.14\"`][tf-pypi] for CPU-only TensorFlow\n1. [`pip install niftynet`](https://pypi.org/project/NiftyNet/)\n\n All other NiftyNet dependencies are installed automatically as part of the pip installation process.\n\nTo install from the source repository, please checkout [the instructions](http://niftynet.readthedocs.io/en/dev/installation.html).\n\n[tf-pypi-gpu]: https://pypi.org/project/tensorflow-gpu/\n[tf-pypi]: https://pypi.org/project/tensorflow/\n\n\n### Documentation\nThe API reference and how-to guides are available on [Read the Docs][rtd-niftynet].\n\n[rtd-niftynet]: http://niftynet.rtfd.io/\n\n### Useful links\n\n* [NiftyNet website][niftynet-io]\n* [NiftyNet source code on GitHub][niftynet-github]\n* [NiftyNet Model zoo repository][niftynet-zoo]\n* [NiftyNet Google Group / Mailing List][ml-niftynet]\n* [Stack Overflow](https://stackoverflow.com/questions/tagged/niftynet) for general questions\n\n[niftynet-io]: http://niftynet.io/\n[niftynet-github]: https://github.com/NifTK/NiftyNet\n[niftynet-zoo]: https://github.com/NifTK/NiftyNetModelZoo/blob/master/README.md\n[ml-niftynet]: https://groups.google.com/forum/#!forum/niftynet\n\n\n### Citing NiftyNet\n\nIf you use NiftyNet in your work, please cite [Gibson and Li, et al. 2018][cmpb2018]:\n\n* 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\u2020 and T. Vercauteren\u2020 (2018)\n[NiftyNet: a deep-learning platform for medical imaging][cmpb2018], _Computer Methods and Programs in Biomedicine_.\nDOI: [10.1016/j.cmpb.2018.01.025][cmpb2018]\n\n\nBibTeX entry:\n\n```\n@article{Gibson2018,\n title = \"NiftyNet: a deep-learning platform for medical imaging\",\n journal = \"Computer Methods and Programs in Biomedicine\",\n year = \"2018\",\n issn = \"0169-2607\",\n doi = \"https://doi.org/10.1016/j.cmpb.2018.01.025\",\n url = \"https://www.sciencedirect.com/science/article/pii/S0169260717311823\",\n author = \"Eli Gibson and Wenqi Li and Carole Sudre and Lucas Fidon and\n Dzhoshkun I. Shakir and Guotai Wang and Zach Eaton-Rosen and\n Robert Gray and Tom Doel and Yipeng Hu and Tom Whyntie and\n Parashkev Nachev and Marc Modat and Dean C. Barratt and\n S\u00e9bastien Ourselin and M. Jorge Cardoso and Tom Vercauteren\",\n}\n```\nThe NiftyNet platform originated in software developed for [Li, et al. 2017][ipmi2017]:\n\n* Li W., Wang G., Fidon L., Ourselin S., Cardoso M.J., Vercauteren T. (2017)\n[On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext Task.][ipmi2017]\nIn: Niethammer M. et al. (eds) Information Processing in Medical Imaging. IPMI 2017.\nLecture Notes in Computer Science, vol 10265. Springer, Cham.\nDOI: [10.1007/978-3-319-59050-9_28][ipmi2017]\n\n\n[ipmi2017]: https://doi.org/10.1007/978-3-319-59050-9_28\n[cmpb2018]: https://doi.org/10.1016/j.cmpb.2018.01.025\n\n\n### Licensing and Copyright\n\nNiftyNet is released under [the Apache License, Version 2.0](https://github.com/NifTK/NiftyNet/blob/dev/LICENSE).\n\nCopyright 2018 the NiftyNet Consortium.\n\n### Acknowledgements\n\nThis project is grateful for the support from\nthe [Wellcome Trust][wt],\nthe [Engineering and Physical Sciences Research Council (EPSRC)][epsrc],\nthe [National Institute for Health Research (NIHR)][nihr],\nthe [Department of Health (DoH)][doh],\n[Cancer Research UK][cruk],\n[King's College London (KCL)][kcl],\n[University College London (UCL)][ucl],\nthe [Science and Engineering South Consortium (SES)][ses],\nthe [STFC Rutherford-Appleton Laboratory][ral], and [NVIDIA][nvidia].\n\n[bmeis]: https://www.kcl.ac.uk/lsm/research/divisions/imaging/index.aspx\n[cmic]: http://cmic.cs.ucl.ac.uk\n[ucl]: http://www.ucl.ac.uk\n[kcl]: http://www.kcl.ac.uk\n[cruk]: https://www.cancerresearchuk.org\n[tf]: https://www.tensorflow.org/\n[weiss]: http://www.ucl.ac.uk/weiss\n[wt]: https://wellcome.ac.uk/\n[epsrc]: https://www.epsrc.ac.uk/\n[nihr]: https://www.nihr.ac.uk/\n[doh]: https://www.gov.uk/government/organisations/department-of-health\n[ses]: https://www.ses.ac.uk/\n[ral]: http://www.stfc.ac.uk/about-us/where-we-work/rutherford-appleton-laboratory/\n[nvidia]: http://www.nvidia.com\n\n\n\n",
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