Metadata-Version: 2.1
Name: clinica
Version: 0.3.5
Summary: Software platform for clinical neuroimaging studies
Home-page: http://www.clinica.run
Author: ARAMIS Lab
Maintainer: Clinica developers
Maintainer-email: clinica-user@inria.fr
License: MIT license
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: End Users/Desktop
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python
Description-Content-Type: text/markdown
Requires-Dist: nibabel (>=2.3.3)
Requires-Dist: nipype (>=1.4.0)
Requires-Dist: pybids (==0.5.1)
Requires-Dist: argcomplete (>=1.9.4)
Requires-Dist: pandas (>=0.24.2)
Requires-Dist: jinja2 (>=2.10.1)
Requires-Dist: xvfbwrapper (==0.2.9)
Requires-Dist: numpy (==1.17)
Requires-Dist: scikit-learn (>=0.20.0)
Requires-Dist: nipy (>=0.4.2)
Requires-Dist: nilearn (>=0.6.0)
Requires-Dist: colorama (>=0.4.1)
Requires-Dist: xgboost (==0.80)
Requires-Dist: xlrd (>=1.2.0)
Requires-Dist: scipy (==1.2.3)
Requires-Dist: matplotlib
Requires-Dist: niflow-nipype1-workflows
Requires-Dist: scikit-image (==0.16.2)
Requires-Dist: torch (>1.0)
Requires-Dist: torchvision

<!--(http://www.clinica.run/img/clinica_brainweb.png)-->

<h1 align="center">
  <a href="http://www.clinica.run">
    <img src="http://www.clinica.run/assets/images/clinica-icon-257x257.png" alt="Logo" width="120" height="120">
  </a>
  <br/>
  Clinica
</h1>

<p align="center"><strong>Software platform for clinical neuroimaging studies</strong></p>

<p align="center">
  <a href="https://ci.inria.fr/clinica-aramis/job/clinica/job/master/">
    <img src="https://ci.inria.fr/clinica-aramis/buildStatus/icon?job=clinica%2Fmaster" alt="Build Status">
  </a>
  <a href="https://badge.fury.io/py/clinica">
    <img src="https://badge.fury.io/py/clinica.svg" alt="PyPI version">
  </a>
  <a href="http://www.clinica.run/doc/Installation/">
    <img src="https://anaconda.org/aramislab/clinica/badges/installer/conda.svg" alt="conda install">
  </a>
  <a href="http://www.clinica.run/doc/Installation/">
    <img src="https://anaconda.org/aramislab/clinica/badges/platforms.svg" alt="platform">
  </a>
</p>

<p align="center">
  <a href="http://www.clinica.run">Homepage</a> |
  <a href="http://www.clinica.run/doc">Documentation</a> |
  <a href="https://groups.google.com/forum/#!forum/clinica-user">Forum</a> |
  See also:
  <a href="#related-repositories">AD-ML</a>,
  <a href="#related-repositories">AD-DL</a>
</p>





## About The Project

Clinica is a software platform for clinical research studies involving patients with neurological and psychiatric diseases and the acquisition of multimodal data (neuroimaging, clinical and cognitive evaluations, genetics...), most often with longitudinal follow-up.

Clinica is command-line driven and written in Python. It uses the [Nipype](https://nipype.readthedocs.io/) system for pipelining and combines widely-used software packages for neuroimaging data analysis ([ANTs](http://stnava.github.io/ANTs/), [FreeSurfer](https://surfer.nmr.mgh.harvard.edu/), [FSL](https://fsl.fmrib.ox.ac.uk/fsl/fslwiki), [MRtrix](https://www.mrtrix.org/), [PETPVC](https://github.com/UCL/PETPVC), [SPM](https://www.fil.ion.ucl.ac.uk/spm/)), machine learning ([Scikit-learn](https://scikit-learn.org/stable/)) and the [BIDS standard](http://bids-specification.readthedocs.io/) for data organization.

Clinica provides tools to convert publicly available neuroimaging datasets into BIDS, namely:

- [ADNI: Alzheimer’s Disease Neuroimaging Initiative](http://www.clinica.run/doc/Converters/ADNI2BIDS)
- [AIBL: Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing](http://www.clinica.run/doc/Converters/AIBL2BIDS)
- [NIFD: Neuroimaging in Frontotemporal Dementia](http://www.clinica.run/doc/Converters/NIFD2BIDS)
- [OASIS: Open Access Series of Imaging Studies](http://www.clinica.run/doc/Converters/OASIS2BIDS)

Clinica can process any BIDS-compliant dataset with a set of complex processing pipelines involving different software packages for the analysis of neuroimaging data (T1-weighted MRI, diffusion MRI and PET data). It also provides integration between feature extraction and statistics, machine learning or deep learning.

![ClinicaPipelines](http://www.clinica.run/img/clinica_pipelines.png)

<p align="center">
  <i>Current pipelines are indicated in blue while new or updated pipelines are indicated in purple (will be released in Summer 2020).</i>
</p>

Clinica is also showcased as a framework for the reproducible classification of Alzheimer's disease using [machine learning](https://github.com/aramis-lab/AD-ML) and [deep learning](https://github.com/aramis-lab/AD-DL).



## Getting Started
> Full instructions for installation and additional information can be found in
the [user documentation](http://www.clinica.run/doc).

Clinica currently supports macOS and Linux. It can be installed:

- With `conda` (recommended):
```sh
conda create --name clinicaEnv python=3.6 clinica -c Aramislab -c conda-forge
```

- With `pip` (needs Python 3.6)
```sh
pip install clinica
```

- Using the [Developer installation](http://www.clinica.run/doc/Installation/#developer-installation)

Depending on the pipeline that you want to use, you need to install pipeline-specific interfaces. Not all the dependencies are necessary to run Clinica. Please refer to this [page](http://www.clinica.run/doc/Third-party/) to determine which third-party libraries you need to install.

## Example

Diagram illustrating the Clinica pipelines involved when performing a group comparison of FDG PET data projected on the cortical surface between patients with Alzheimer's disease and healthy controls from the ADNI database:
![ClinicaExample](http://www.clinica.run/img/clinica_example.png)
1. Clinical and neuroimaging data are downloaded from the ADNI website and data are converted into BIDS with the [`adni-to-bids` converter](http://www.clinica.run/doc/Converters/ADNI2BIDS).
2. Estimation of the cortical and white surface is then produced by the [`t1-freesurfer` pipeline](http://www.clinica.run/doc/Pipelines/T1_FreeSurfer).
3. FDG PET data can be projected on the subject’s cortical surface and normalized to the FsAverage template from FreeSurfer using the [`pet-surface` pipeline](http://www.clinica.run/doc/Pipelines/PET_Surface).
4. TSV file with demographic information of the population studied is given to the [`statistics-surface` pipeline](http://www.clinica.run/doc/Pipelines/Stats_Surface) to generate the results of the group comparison.

> For more examples and details, please refer to the [Documentation](http://www.clinica.run/doc/).





## Support
- [Report an issue on GitHub](https://github.com/aramis-lab/clinica/issues)
- Use the [Clinica Google Group](https://groups.google.com/forum/#!forum/clinica-user) to ask for help!





<!--
## Contributing
We encourage you to contribute to Clinica! Please check out the [Contributing to Clinica guide](Contributing.md) for guidelines about how to proceed. Do not hesitate to ask questions if something is not clear for you, report an issue, etc.
-->




## License

This software is distributed under the MIT License. See [license file](https://github.com/aramis-lab/clinica/blob/dev/LICENSE.txt) for more information.


## Related Repositories

- [AD-DL: Framework for the reproducible classification of Alzheimer's disease using deep learning](https://github.com/aramis-lab/AD-DL)
- [AD-ML: Framework for the reproducible classification of Alzheimer's disease using machine learning](https://github.com/aramis-lab/AD-ML)


