|Project Name||Stars||Downloads||Repos Using This||Packages Using This||Most Recent Commit||Total Releases||Latest Release||Open Issues||License||Language|
|Mne Python||2,406||99||185||3 days ago||76||November 20, 2023||478||bsd-3-clause||Python|
|MNE: Magnetoencephalography (MEG) and Electroencephalography (EEG) in Python|
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|DIPY is the paragon 3D/4D+ imaging library in Python. Contains generic methods for spatial normalization, signal processing, machine learning, statistical analysis and visualization of medical images. Additionally, it contains specialized methods for computational anatomy including diffusion, perfusion and structural imaging.|
|Brainiak||315||1||14 days ago||15||October 15, 2020||87||apache-2.0||Python|
|Brain Imaging Analysis Kit|
|Mriqc||249||1||9 days ago||104||June 14, 2023||88||apache-2.0||Python|
|Automated Quality Control and visual reports for Quality Assessment of structural (T1w, T2w) and functional MRI of the brain|
|Clinica||195||1||4 days ago||36||September 08, 2023||59||other||Python|
|Software platform for clinical neuroimaging studies|
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|A framework for developing neural network models for 3D image processing.|
|Canlabcore||133||2 days ago||28||other||MATLAB|
|Core tools required for running Canlab Matlab toolboxes. The heart of this toolbox is object-oriented tools that enable interactive analysis of neuroimaging data and simple scripts using high-level commands tailored to neuroimaging analysis.|
|Nltools||116||1||2||a month ago||40||October 31, 2023||61||mit||Python|
|Python toolbox for analyzing imaging data|
Software platform for clinical neuroimaging studies
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 system for pipelining and combines widely-used software packages for neuroimaging data analysis (ANTs, FreeSurfer, FSL, MRtrix, PETPVC, SPM), machine learning (Scikit-learn) and the BIDS standard for data organization.
Clinica provides tools to convert publicly available neuroimaging datasets into BIDS, namely:
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.
Full instructions for installation and additional information can be found in the user documentation.
Clinica can be easily installed and updated using pipx.
pipx install clinica
pip install clinica
Clinica relies on multiple third-party tools to perform processing.
An environment file is provided in this repository to facilitate their installation in a Conda environment:
git clone https://github.com/aramis-lab/clinica && cd clinica conda env create conda activate clinica
After activation, use
pip to install Clinica.
Depending on the pipeline that you want to use, you need to install pipeline-specific interfaces. Some of which uses a different runtime or use incompatible licensing terms, which prevent their distribution alongside Clinica. Not all the dependencies are necessary to run Clinica. Please refer to this page to determine which third-party libraries you need to install.
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:
statistics-surfacepipeline to generate the results of the group comparison.
For more examples and details, please refer to the Documentation.
We encourage you to contribute to Clinica! Please check out the Contributing to Clinica guide for guidelines about how to proceed. Do not hesitate to ask questions if something is not clear for you, report an issue, etc.
This software is distributed under the MIT License. See license file for more information.