Metadata-Version: 2.1
Name: clinica
Version: 0.4.1
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
Description: <!--(http://www.clinica.run/img/clinica_brainweb.png)-->
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        <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="https://aramislab.paris.inria.fr/clinica/docs/public/latest/Installation/">
          </a>
          <a href="https://aramislab.paris.inria.fr/clinica/docs/public/latest/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="https://aramislab.paris.inria.fr/clinica/docs/public/latest/">Documentation</a> |
          <a href="https://hal.inria.fr/hal-02308126">Preprint</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](https://aramislab.paris.inria.fr/clinica/docs/public/latest/Converters/ADNI2BIDS/)
        - [AIBL: Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing](https://aramislab.paris.inria.fr/clinica/docs/public/latest/Converters/AIBL2BIDS/)
        - [NIFD: Neuroimaging in Frontotemporal Dementia](https://aramislab.paris.inria.fr/clinica/docs/public/latest/Converters/NIFD2BIDS/)
        - [OASIS: Open Access Series of Imaging Studies](https://aramislab.paris.inria.fr/clinica/docs/public/latest/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_A4_2021-04-02_75dpi.jpg)
        
        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](https://aramislab.paris.inria.fr/clinica/docs/public/latest/).
        
        Clinica currently supports macOS and Linux.
        It can be installed by typing the following command:
        
        ```sh
        pip install clinica
        ```
        
        To avoid conflicts with other versions of the dependency packages installed by pip, it is strongly recommended to create a virtual environment before the installation.
        For example, use [Conda](https://docs.conda.io/en/latest/miniconda.html), to create a virtual
        environment and activate it before installing clinica (you can also use
        `virtualenv`):
        
        ```sh
        conda create --name clinicaEnv python=3.7
        conda activate clinicaEnv
        ```
        
        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](https://aramislab.paris.inria.fr/clinica/docs/public/latest/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_2021-04-02_75dpi.jpg)
        
        1. Clinical and neuroimaging data are downloaded from the ADNI website and data
           are converted into BIDS with the [`adni-to-bids`
           converter](https://aramislab.paris.inria.fr/clinica/docs/public/latest/Converters/ADNI2BIDS/).
        2. Estimation of the cortical and white surface is then produced by the
           [`t1-freesurfer`
           pipeline](https://aramislab.paris.inria.fr/clinica/docs/public/latest/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](https://aramislab.paris.inria.fr/clinica/docs/public/latest/Pipelines/PET_Surface/).
        4. TSV file with demographic information of the population studied is given to
           the [`statistics-surface`
           pipeline](https://aramislab.paris.inria.fr/clinica/docs/public/latest/Pipelines/Stats_Surface/) to generate
           the results of the group comparison.
        
        > For more examples and details, please refer to the
        > [Documentation](https://aramislab.paris.inria.fr/clinica/docs/public/latest/).
        
        ## 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)
        
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
Requires-Python: >=3.6,<3.8
Description-Content-Type: text/markdown
Provides-Extra: test
