updating documentation

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diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
@@ -1,8 +1,6 @@
 # Contributor guide
-The main source code repository for NiftyNet is [GitHub][github-niftynet].
-The NiftyNet codebase is also mirrored on [CMICLab][cmiclab-niftynet].
+The source code for NiftyNet is released via [GitHub][github-niftynet].
 
-[cmiclab-niftynet]: https://cmiclab.cs.ucl.ac.uk/CMIC/NiftyNet
 [github-niftynet]: https://github.com/NifTK/NiftyNet
 
 
@@ -47,7 +45,7 @@ sh run_test.sh
 ```
 
 ### 3. Creating GitHub pull requests
-1. **[on GitHub]** Sign up/in GitHub.com (The rest steps assume GitHub user id: `nntestuser`).
+1. **[on GitHub]** Sign up/in [GitHub.com](https://github.com/) (The rest steps assume GitHub user id: `nntestuser`).
 1. **[on GitHub]** Go to [https://github.com/NifTK/NiftyNet](https://github.com/NifTK/NiftyNet), click the 'Fork' button.
 1. Download the repo:
    * `git clone https://github.com/nntestuser/NiftyNet.git`
@@ -70,6 +68,10 @@ sh run_test.sh
 *This section describes steps to create unit tests for NiftyNet.*
 
 #### 1. Determine which module to test
+Go to [Gitlab pipeline](https://gitlab.com/NifTK/NiftyNet/pipelines) page,
+click on the latest successful testing pipeline and check the test coverage report at the bottom of the test log.
+The coverage report lists all untested files (with line numbers of specific statements) in the project.
+
 
 #### 2. File an issue
 Create a new issue indicating that you'll be working on the tests of a particular module.
@@ -216,7 +218,7 @@ This creates a [wheel binary package][wheel-binary] in a newly created `dist` di
 
 [niftynet-cmiclab]: https://cmiclab.cs.ucl.ac.uk/CMIC/NiftyNet
 [git-tag]: https://git-scm.com/book/en/v2/Git-Basics-Tagging
-[pip-camera-ready]: https://cmiclab.cs.ucl.ac.uk/CMIC/NiftyNet/blob/940d7a827d6835a4ce10637014c0c36b3c980476/.gitlab-ci.yml#L323
+[pip-camera-ready]: https://github.com/niftk/NiftyNet/blob/940d7a827d6835a4ce10637014c0c36b3c980476/.gitlab-ci.yml#L323
 [pip-camera-ready-output]: https://cmiclab.cs.ucl.ac.uk/CMIC/NiftyNet/-/jobs/30450
 [python-setuptools]: https://packaging.python.org/tutorials/distributing-packages/#wheels
 [wheel-binary]: https://www.python.org/dev/peps/pep-0491/
@@ -229,9 +231,4 @@ For an example how to do this please see [lines 223 to 270 in the `.gitlab-ci.ym
 
 [pip-console-entry]: http://python-packaging.readthedocs.io/en/latest/command-line-scripts.html#the-console-scripts-entry-point
 [gitlab-ci-yaml]: https://docs.gitlab.com/ce/ci/yaml/
-[gitlab-ci-pip-installer-test]: https://cmiclab.cs.ucl.ac.uk/CMIC/NiftyNet/blob/940d7a827d6835a4ce10637014c0c36b3c980476/.gitlab-ci.yml#L223
-
-
-Go to [Cmiclab pipeline](https://cmiclab.cs.ucl.ac.uk/CMIC/NiftyNet/pipelines) page,
-click on the latest successful testing pipeline and check the test coverage report at the bottom of the test log, e.g. a coverage report is available at the last part of this [log](https://cmiclab.cs.ucl.ac.uk/CMIC/NiftyNet/-/jobs/35553).
-The coverage report lists all untested files (with line numbers of specific statements) in the project.
+[gitlab-ci-pip-installer-test]: https://github.com/niftk/NiftyNet/blob/940d7a827d6835a4ce10637014c0c36b3c980476/.gitlab-ci.yml#L223
diff --git a/README.md b/README.md
@@ -1,10 +1,10 @@
 # NiftyNet
 
-<img src="https://cmiclab.cs.ucl.ac.uk/CMIC/NiftyNet/raw/master/niftynet-logo.png" width="263" height="155">
+<img src="https://github.com/NifTK/NiftyNet/raw/dev/niftynet-logo.png" width="263" height="155">
 
-[![build status](https://cmiclab.cs.ucl.ac.uk/CMIC/NiftyNet/badges/dev/build.svg)](https://cmiclab.cs.ucl.ac.uk/CMIC/NiftyNet/commits/dev)
-[![coverage report](https://cmiclab.cs.ucl.ac.uk/CMIC/NiftyNet/badges/dev/coverage.svg)](https://cmiclab.cs.ucl.ac.uk/CMIC/NiftyNet/commits/dev)
-[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://cmiclab.cs.ucl.ac.uk/CMIC/NiftyNet/blob/dev/LICENSE)
+[![pipeline status](https://gitlab.com/NifTK/NiftyNet/badges/dev/pipeline.svg)](https://github.com/NifTK/NiftyNet/commits/dev)
+[![coverage report](https://gitlab.com/NifTK/NiftyNet/badges/dev/coverage.svg)](https://github.com/NifTK/NiftyNet)
+[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://github.com/NifTK/NiftyNet/blob/dev/LICENSE)
 [![PyPI version](https://badge.fury.io/py/NiftyNet.svg)](https://badge.fury.io/py/NiftyNet)
 
 NiftyNet is a [TensorFlow][tf]-based open-source convolutional neural networks (CNN) platform for research in medical image analysis and image-guided therapy.
@@ -20,22 +20,20 @@ NiftyNet is a consortium of research groups (WEISS -- [Wellcome EPSRC Centre for
 
 ### Features
 
-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
+* Efficient training with multiple-GPU support
 * Implementation of recent networks (HighRes3DNet, 3D U-net, V-net, DeepMedic)
 * Comprehensive evaluation metrics for medical image segmentation
 
+ <sup>NiftyNet is not intended for clinical use.</sup>
+
+ <sup>NiftyNet release notes are available [here][changelog].</sup>
+
  <sup>*2.5-D: volumetric images processed as a stack of 2D slices;
 4-D: co-registered multi-modal 3D volumes</sup>
 
-NiftyNet release notes are available [here][changelog].
-
 [changelog]: CHANGELOG.md
 
 
@@ -46,9 +44,9 @@ NiftyNet release notes are available [here][changelog].
    * [`pip install tensorflow==1.7`][tf-pypi] for CPU-only TensorFlow
 1. [`pip install niftynet`](https://pypi.org/project/NiftyNet/)
 
- <sup>*All other NiftyNet dependencies are installed automatically as part of the pip installation process.
- 
-*To install from the source repository, please checkout [the instructions](http://niftynet.readthedocs.io/en/dev/installation.html).</sup>
+ <sup>All other NiftyNet dependencies are installed automatically as part of the pip installation process.
+
+To install from the source repository, please checkout [the instructions](http://niftynet.readthedocs.io/en/dev/installation.html).</sup>
 
 [tf-pypi-gpu]: https://pypi.org/project/tensorflow-gpu/
 [tf-pypi]: https://pypi.org/project/tensorflow/
@@ -63,20 +61,17 @@ The API reference and how-to guides are available on [Read the Docs][rtd-niftyne
 
 [NiftyNet website][niftynet-io]
 
-[NiftyNet source code on CmicLab][niftynet-cmiclab]
-
-[NiftyNet source code mirror on GitHub][niftynet-github]
+[NiftyNet source code on GitHub][niftynet-github]
 
-[Model zoo repository][niftynet-zoo]
+[NiftyNet Model zoo repository][niftynet-zoo]
 
 NiftyNet mailing list: [nifty-net@live.ucl.ac.uk][ml-niftynet]
 
 [Stack Overflow](https://stackoverflow.com/questions/tagged/niftynet) for general questions
 
 [niftynet-io]: http://niftynet.io/
-[niftynet-cmiclab]: https://cmiclab.cs.ucl.ac.uk/CMIC/NiftyNet
 [niftynet-github]: https://github.com/NifTK/NiftyNet
-[niftynet-zoo]: https://cmiclab.cs.ucl.ac.uk/CMIC/NiftyNetExampleServer/blob/master/model_zoo.md
+[niftynet-zoo]: https://github.com/NifTK/NiftyNetModelZoo/blob/master/README.md
 [ml-niftynet]: mailto:nifty-net@live.ucl.ac.uk
 
 
@@ -121,15 +116,17 @@ DOI: [10.1007/978-3-319-59050-9_28][ipmi2017]
 
 ### Licensing and Copyright
 
-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.
+NiftyNet is released under [the Apache License, Version 2.0](https://github.com/NifTK/NiftyNet/blob/dev/LICENSE).
+
+Copyright 2018 the NiftyNet Consortium.
 
 ### Acknowledgements
 
-This project is grateful for the support from the [Wellcome Trust][wt], the [Engineering and Physical Sciences Research Council (EPSRC)][epsrc], the [National Institute for Health Research (NIHR)][nihr], the [Department of Health (DoH)][doh], [Cancer Research UK][cruk], [University College London (UCL)][ucl], the [Science and Engineering South Consortium (SES)][ses], the [STFC Rutherford-Appleton Laboratory][ral], and [NVIDIA][nvidia].
+This project is grateful for the support from the [Wellcome Trust][wt], the [Engineering and Physical Sciences Research Council (EPSRC)][epsrc], the [National Institute for Health Research (NIHR)][nihr], the [Department of Health (DoH)][doh], [Cancer Research UK][cruk], [University College London (UCL)][ucl], [King's College London (KCL)][kcl], the [Science and Engineering South Consortium (SES)][ses], the [STFC Rutherford-Appleton Laboratory][ral], and [NVIDIA][nvidia].
 
 [cmic]: http://cmic.cs.ucl.ac.uk
 [ucl]: http://www.ucl.ac.uk
+[kcl]: http://www.kcl.ac.uk
 [cruk]: https://www.cancerresearchuk.org
 [tf]: https://www.tensorflow.org/
 [weiss]: http://www.ucl.ac.uk/weiss

diff --git a/demos/BRATS17/README.md b/demos/BRATS17/README.md
@@ -19,7 +19,7 @@ and segmentation probability map using this demo [1].
 *[1] This method ranked the first (in terms of averaged Dice score 0.90499) according
 to the online validation leaderboard of [BRATS challenge 2017](https://www.cbica.upenn.edu/BraTS17/lboardValidation.html).*
 
-_Please checkout a trained model in [NiftyNet model zoo](https://cmiclab.cs.ucl.ac.uk/CMIC/NiftyNetExampleServer/blob/master/anisotropic_nets_brats_challenge_model_zoo.md)._
+_Please checkout a trained model in [NiftyNet model zoo](https://github.com/NifTK/NiftyNetModelZoo/blob/master/anisotropic_nets_brats_challenge_model_zoo.md)._
 
 
 Model training requires

diff --git a/demos/GAN/README.md b/demos/GAN/README.md
@@ -1,2 +1,2 @@
-_Please checkout a trained model in [NiftyNet model zoo](https://cmiclab.cs.ucl.ac.uk/CMIC/NiftyNetExampleServer/blob/master/ultrasound_simulator_gan_model_zoo.md)._
+_Please checkout a trained model in [NiftyNet model zoo](https://github.com/NifTK/NiftyNetModelZoo/blob/master/ultrasound_simulator_gan_model_zoo.md)._
 
diff --git a/demos/PROMISE12/README.md b/demos/PROMISE12/README.md
@@ -16,7 +16,7 @@ conda install nb_conda_kernels
 jupyter notebook
 ```
 
-_This demo only supports NiftyNet cloned from [GitHub](https://github.com/NifTK/NiftyNet) or [CMICLab](https://cmiclab.cs.ucl.ac.uk/CMIC/NiftyNet)_
+_This demo only supports NiftyNet cloned from [GitHub](https://github.com/NifTK/NiftyNet)_
 
-Please find further demos/trained models at [NiftyNet model zoo](https://cmiclab.cs.ucl.ac.uk/CMIC/NiftyNetExampleServer/blob/master/dense_vnet_abdominal_ct_model_zoo.md).
+Please find further demos/trained models at [NiftyNet model zoo](https://github.com/NifTK/NiftyNetModelZoo/blob/master/dense_vnet_abdominal_ct_model_zoo.md).
 
diff --git a/demos/README.md b/demos/README.md
@@ -1,4 +1,4 @@
-* Please checkout [NiftyNet model zoo](https://cmiclab.cs.ucl.ac.uk/CMIC/NiftyNetExampleServer/blob/master/model_zoo.md) for demos and trained models.
+* Please checkout [NiftyNet model zoo](https://github.com/NifTK/NiftyNetModelZoo/blob/master/README.md) for demos and trained models.
 
 * Please checkout [NiftyNet documentation website](http://niftynet.readthedocs.io/en/dev/config_spec.html) for configuration file usage.
 
diff --git a/demos/brain_parcellation/README.md b/demos/brain_parcellation/README.md
@@ -18,5 +18,5 @@ This demo will download an MR volume and a
 trained network model, and then using the NiftyNet inference program to
 generate brain parcellation.
 
-_Please visit the [NiftyNet model zoo entry](https://cmiclab.cs.ucl.ac.uk/CMIC/NiftyNetExampleServer/blob/master/highres3dnet_brain_parcellation_model_zoo.md)
+_Please visit the [NiftyNet model zoo entry](https://github.com/NifTK/NiftyNetModelZoo/blob/master/highres3dnet_brain_parcellation_model_zoo.md)
 for more information on running this demo._
diff --git a/demos/module_examples/README.md b/demos/module_examples/README.md
@@ -16,7 +16,4 @@ conda install nb_conda_kernels
 jupyter notebook
 ```
 
-_This demo only supports NiftyNet cloned from [GitHub](https://github.com/NifTK/NiftyNet) or [CMICLab](https://cmiclab.cs.ucl.ac.uk/CMIC/NiftyNet)_
-
-Please find further demos/trained models at [NiftyNet model zoo](https://cmiclab.cs.ucl.ac.uk/CMIC/NiftyNetExampleServer/blob/master/dense_vnet_abdominal_ct_model_zoo.md).
-
+_This demo only supports NiftyNet cloned from [GitHub](https://github.com/NifTK/NiftyNet)_
diff --git a/demos/variational_autoencoder/README.md b/demos/variational_autoencoder/README.md
@@ -67,7 +67,7 @@ cd ${demopath};
 net_autoencoder train -c ${demopath}/vae_config.ini
 ```
 
-or using NiftyNet cloned from [GitHub](https://github.com/NifTK/NiftyNet) or [CMICLab](https://cmiclab.cs.ucl.ac.uk/CMIC/NiftyNet):
+or using NiftyNet cloned from [GitHub](https://github.com/NifTK/NiftyNet):
 ```bash
 cd NiftyNet/
 # train a variational autoencoder
@@ -87,7 +87,7 @@ net_autoencoder inference -c ${demopath}/vae_config.ini \
         --inference_iter -1
 ```
 
-or using NiftyNet cloned from [GitHub](https://github.com/NifTK/NiftyNet) or [CMICLab](https://cmiclab.cs.ucl.ac.uk/CMIC/NiftyNet):
+or using NiftyNet cloned from [GitHub](https://github.com/NifTK/NiftyNet):
 ```bash
 cd NiftyNet;
 # encode each image using the latest trained model,
@@ -111,7 +111,7 @@ net_autoencoder inference -c ${demopath}/vae_config.ini \
         --inference_iter -1
 ```
 
-or using NiftyNet cloned from [GitHub](https://github.com/NifTK/NiftyNet) or [CMICLab](https://cmiclab.cs.ucl.ac.uk/CMIC/NiftyNet):
+or using NiftyNet cloned from [GitHub](https://github.com/NifTK/NiftyNet):
 ```bash
 cd NiftyNet;
 # decode image embedding using the latest trained model,

diff --git a/doc/source/conf.py b/doc/source/conf.py
@@ -126,8 +126,8 @@ def setup(app):
 
 # General information about the project.
 project = u'NiftyNet'
-copyright = u'2018, NiftyNet Consortium'
-author = u'NiftyNet Consortium'
+copyright = u'2018, the NiftyNet Consortium'
+author = u'the NiftyNet Consortium'
 
 # The version info for the project you're documenting, acts as replacement for
 # |version| and |release|, also used in various other places throughout the

diff --git a/doc/source/contributing.md b/doc/source/contributing.md
@@ -1,8 +1,6 @@
 # Contributor guide
-The main source code repository for NiftyNet is [GitHub][github-niftynet].
-The NiftyNet codebase is also mirrored on [CMICLab][cmiclab-niftynet].
+The source code for NiftyNet is released via [GitHub][github-niftynet].
 
-[cmiclab-niftynet]: https://cmiclab.cs.ucl.ac.uk/CMIC/NiftyNet
 [github-niftynet]: https://github.com/NifTK/NiftyNet
 
 
@@ -47,7 +45,7 @@ sh run_test.sh
 ```
 
 ### 3. Creating GitHub pull requests
-1. **[on GitHub]** Sign up/in GitHub.com (The rest steps assume GitHub user id: `nntestuser`).
+1. **[on GitHub]** Sign up/in [GitHub.com](https://github.com/) (The rest steps assume GitHub user id: `nntestuser`).
 1. **[on GitHub]** Go to [https://github.com/NifTK/NiftyNet](https://github.com/NifTK/NiftyNet), click the 'Fork' button.
 1. Download the repo:
    * `git clone https://github.com/nntestuser/NiftyNet.git`
@@ -70,6 +68,10 @@ sh run_test.sh
 *This section describes steps to create unit tests for NiftyNet.*
 
 #### 1. Determine which module to test
+Go to [Gitlab pipeline](https://gitlab.com/NifTK/NiftyNet/pipelines) page,
+click on the latest successful testing pipeline and check the test coverage report at the bottom of the test log.
+The coverage report lists all untested files (with line numbers of specific statements) in the project.
+
 
 #### 2. File an issue
 Create a new issue indicating that you'll be working on the tests of a particular module.
@@ -201,37 +203,3 @@ For a practical example see [how the `net_segment` CLI command is implemented][n
 
 [net-segment-entry]: https://github.com/NifTK/NiftyNet/blob/v0.3.0/setup.py#L107
 
-
-
-## Deprecated instructions
-
-### Bundling a pip installer
-
-The NiftyNet pip installer gets bundled automatically for [Git tags][git-tag] starting with a `v` (for "version") pushed to [CMICLab][niftynet-cmiclab].
-The [wheel version][wheel-version-tag] is determined automatically as part of this process.
-To see how this is done in practice, please go to the [`pip-camera-ready` section of `.gitlab-ci.yml`][pip-camera-ready] (and see the result in [this build log - esp. the last few lines lines, which show where the pip installer can be found on the build server][pip-camera-ready-output]).
-
-In particular, bundling a pip installer boils down to running the command [`python setup.py bdist_wheel`][python-setuptools] in the top-level directory.
-This creates a [wheel binary package][wheel-binary] in a newly created `dist` directory, e.g. `dist/NiftyNet-0.2.0-py2.py3-none-any.whl`.
-
-[niftynet-cmiclab]: https://cmiclab.cs.ucl.ac.uk/CMIC/NiftyNet
-[git-tag]: https://git-scm.com/book/en/v2/Git-Basics-Tagging
-[pip-camera-ready]: https://cmiclab.cs.ucl.ac.uk/CMIC/NiftyNet/blob/940d7a827d6835a4ce10637014c0c36b3c980476/.gitlab-ci.yml#L323
-[pip-camera-ready-output]: https://cmiclab.cs.ucl.ac.uk/CMIC/NiftyNet/-/jobs/30450
-[python-setuptools]: https://packaging.python.org/tutorials/distributing-packages/#wheels
-[wheel-binary]: https://www.python.org/dev/peps/pep-0491/
-
-
-**If you have made changes to the pip installer, please test these.**
-For instance if you have added a new [CLI entry point][pip-console-entry]  (i.e. a new "command" - also see the respective section below),
-make sure you include the appropriate tests in the [GitLab CI configuration][gitlab-ci-yaml].
-For an example how to do this please see [lines 223 to 270 in the `.gitlab-ci.yml` file][gitlab-ci-pip-installer-test].
-
-[pip-console-entry]: http://python-packaging.readthedocs.io/en/latest/command-line-scripts.html#the-console-scripts-entry-point
-[gitlab-ci-yaml]: https://docs.gitlab.com/ce/ci/yaml/
-[gitlab-ci-pip-installer-test]: https://cmiclab.cs.ucl.ac.uk/CMIC/NiftyNet/blob/940d7a827d6835a4ce10637014c0c36b3c980476/.gitlab-ci.yml#L223
-
-
-Go to [Cmiclab pipeline](https://cmiclab.cs.ucl.ac.uk/CMIC/NiftyNet/pipelines) page,
-click on the latest successful testing pipeline and check the test coverage report at the bottom of the test log, e.g. a coverage report is available at the last part of this [log](https://cmiclab.cs.ucl.ac.uk/CMIC/NiftyNet/-/jobs/35553).
-The coverage report lists all untested files (with line numbers of specific statements) in the project.