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Clinica

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.

The development of Clinica was initiated by the ARAMIS Lab at the Paris Brain Institute.

We are very pleased to announce that

Clinica 0.7.0 is out!

What are the main features of Clinica?

• Complex processing pipelines involving different software packages.
• Integration between feature extraction and statistics / machine learning.
• Standardized input/output data structures.
• Conversion of publicly available datasets (ADNI, AIBL, OASIS, NIFD) to BIDS.

Why should I install Clinica?

In short: to make your life easier!

With Clinica you can:
• easily share data and results within your institution and with external collaborators;
• make your research more reproducible;
• spend less time on data management and processing.

Who are the intended users of Clinica?

Clinica is meant for users looking for a straightforward and efficient way to process and analyze neuroimaging data, such as machine learning experts wishing to work with neuroimages or clinical fellows not familiar with image processing and analysis tools.

Which technologies underlie Clinica?

Clinica is written in Python. It uses Nipype for pipelining and combines widely-used software packages for neuroimaging data analysis (SPM, FSL,  FreeSurferMRtrix, ...), machine learning (Scikit-learn) and the BIDS standard for data organization.
Its deep learning companion, ClinicaDL, relies on PyTorch.

Around Clinica

ClinicaDL

Framework for the reproducible processing of neuroimaging data with deep learning

Tutorial

Deep learning classification from brain MRI: Application to Alzheimer’s disease

AD-DL

Framework for the reproducible evaluation of deep learning classification experiments using anatomical MRI data for the computer-aided diagnosis of Alzheimer's disease

AD-ML

Framework for the reproducible evaluation of machine learning classification experiments using anatomical MRI and PET data for the computer-aided diagnosis of Alzheimer's disease

Citing us

If you are using Clinica, please cite:

  • A. Routier, N. Burgos, M. Díaz, M. Bacci, S. Bottani, O. El Rifai, S. Fontanella, P. Gori, J. Guillon, A. Guyot, R. Hassanaly, T. Jacquemont, P. Lu, A. Marcoux, T. Moreau, J. Samper-González, M. Teichmann. E. Thibeau-Sutre, G. Vaillant, J. Wen, A. Wild, M.-O. Habert, S. Durrleman, O. Colliot – Clinica: an open source software platform for reproducible clinical neuroscience studies. Frontiers in Neuroinformatics, 2021 [Publisher | Open access]

Depending on the tools you are using, we may ask you to cite one or several of the following papers:

  • J. Samper-González, N. Burgos, S. Bottani, S. Fontanella, P. Lu, A. Marcoux, A. Routier, J. Guillon, M. Bacci, J. Wen, A. Bertrand, H. Bertin, M.-O. Habert, S. Durrleman, T. Evgeniou, O. Colliot – Reproducible evaluation of classification methods in Alzheimer’s disease: Framework and application to MRI and PET data. NeuroImage, 2018 [Publisher | Open access]
  • J. Wen, J. Samper-González, S. Bottani, A. Routier, N. Burgos, T. Jacquemont, S. Fontanella, S. Durrleman, S. Epelbaum, A. Bertrand, O. Colliot – Reproducible Evaluation of Diffusion MRI Features for Automatic Classification of Patients with Alzheimer’s Disease. Neuroinformatics, 2020 [Publisher | Open access]
  • J. Wen, E. Thibeau-Sutre, M. Diaz-Melo, J. Samper-González, A. Routier, S. Bottani, D. Dormont, S. Durrleman, N. Burgos and O. Colliot – Convolutional neural networks for classification of Alzheimer’s disease: Overview and reproducible evaluation. Medical Image Analysis, 2020 [Publisher | Open access]
  • A. Marcoux, N. Burgos, A. Bertrand, M. Teichmann, A. Routier, J. Wen, J. Samper-González, S. Bottani, S. Durrleman, M.-O. Habert, O. Colliot – An Automated Pipeline for the Analysis of PET Data on the Cortical Surface. Frontiers in Neuroinformatics, 2018 [Publisher | Open access]

The papers that should be cited are mentioned in the "Describing this pipeline in your paper" section in the documentation of each pipeline.

Contributors

Former members of the Clinica team appear in italics.

Olivier Colliot

• Project coordinator
• Co-founder

Stanley Durrleman

• Co-founder

Ninon Burgos

• Project coordinator
• Documentation

Michael Bacci

• Software architecture
• Optimizations
• Command-line system
• Test / Benchmarks / Profiling

Simona Bottani

• Atlas-based measurements
• Machine learning
• Dataset converters

Mauricio Díaz

• Deep Learning
• Software architecture
• Optimizations
• Continuous integration
• Test / Benchmarks / Profiling

Omar El Rifai

• Software architecture
• Optimizations
• Continuous integration
• Test / Benchmarks / Profiling

Sabrina Fontanella

• I/O tools
• Dataset converters
• Data structure specifications

Pietro Gori

• Diffusion MRI analysis

Jérémy Guillon

• Software Architecture
• Optimizations
• Functional MRI analysis
• Diffusion MRI analysis

Alexis Guyot

• Anatomical MRI analysis
• Longitudinal analysis
• Shape analysis with Deformetrica
• Test / Benchmarks / Profiling

Ravi Hassanaly

• PET data analysis
• Deep learning

Thomas Jacquemont

• Diffusion MRI analysis
• Atlas-based measurements

Matthieu Joulot

• Dataset converters
• Anatomical MRI analysis

Pascal Lu

• Machine learning

Arnaud Marcoux

• Software Architecture
• Continuous integration
• PET data analysis
• Longitudinal analysis
• Test / Benchmarks / Profiling

Tristan Moreau

• Diffusion MRI analysis

Alexandre Routier

• Software Architecture
• Anatomical MRI analysis
• Diffusion MRI analysis
• Surface-based statistics
• Longitudinal analysis
• CAPS data structure specifications
• Project management
• Documentation
• Website
• Test / Benchmarks / Profiling

Jorge Samper-Gonzalez

• Anatomical MRI analysis
• PET data analysis
• Machine learning
• Dataset converters
• Test / Benchmarks / Profiling

Elina Thibeau-Sutre

• I/O tools
• Dataset converters
• Deep learning

Ghislain Vaillant

• ClinicaCloud
• Software architecture
• Optimizations
• Test / Benchmarks / Profiling

Junhao Wen

• Anatomical MRI analysis
• Diffusion MRI analysis
• Surface-based statistics
• Machine learning
• Deep learning
• Test / Benchmarks / Profiling

Adam Wild

• Dataset converters

Clinica is a software platform for research studies.
It is not intended for use in medical routine.

About ARAMIS Lab

ARAMIS Lab is a pluridisciplinary group bringing together methodological researchers (computer science, applied mathematics) and medical experts (neurology, medical imaging).

Address

Institut du Cerveau - Paris Brain Institute
Équipe Aramis, 3eme étage
Hôpital de la Pitié-Salpêtrière
47, boulevard de l’hôpital
75013 Paris
France

Mobirise

Copyright (c) 2016-2021 Clinica