clinicadl 1.6.1

Framework for the reproducible processing of neuroimaging data with deep learning methods

ClinicaDL

Framework for the reproducible processing of neuroimaging data with deep learning methods

Documentation | Tutorial | Forum

About the project

This repository hosts ClinicaDL, the deep learning extension of Clinica, a python library to process neuroimaging data in BIDS format.

Disclaimer: this software is under development. Some features can change between different releases and/or commits.

To access the full documentation of the project, follow the link https://clinicadl.readthedocs.io/. If you find a problem when using it or if you want to provide us feedback, please open an issue or write on the forum.

Getting started

ClinicaDL currently supports macOS and Linux.

We recommend to use conda or virtualenv for the installation of ClinicaDL as it guarantees the correct management of libraries depending on common packages:

conda create --name ClinicaDL python=3.8
conda activate ClinicaDL
pip install clinicadl

Tutorial

Visit our hands-on tutorial web site to start using ClinicaDL directly in a Google Colab instance!

Related Repositories

• Clinica: Software platform for clinical neuroimaging studies
• AD-DL: Convolutional neural networks for classification of Alzheimer's disease: Overview and reproducible evaluation
• AD-ML: Framework for the reproducible classification of Alzheimer's disease using machine learning

Citing us

• Thibeau-Sutre, E., Díaz, M., Hassanaly, R., Routier, A., Dormont, D., Colliot, O., Burgos, N.: ‘ClinicaDL: an open-source deep learning software for reproducible neuroimaging processing‘, 2021. hal-03351976
• Routier, A., Burgos, N., Díaz, M., Bacci, M., Bottani, S., El-Rifai O., Fontanella, S., Gori, P., Guillon, J., Guyot, A., Hassanaly, R., Jacquemont, T., Lu, P., Marcoux, A., Moreau, T., Samper-González, J., Teichmann, M., Thibeau-Sutre, E., Vaillant G., Wen, J., Wild, A., Habert, M.-O., Durrleman, S., and Colliot, O.: ‘Clinica: An Open Source Software Platform for Reproducible Clinical Neuroscience Studies’, 2021. doi:10.3389/fninf.2021.689675 Open Access version