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Open access platform for MRI brain analysis


The volBrain platform is an online open MRI brain volumetry system. Our mission is to help researchers all over the world to obtain automatically volumetric brain information from their MRI data without the need of learning complex software packages or having expensive computational infrastructures in their local sites.

The volBrain platform works in a fully automatic manner and is able to provide brain analysis without any human interaction in few minutes. We have currently deployed pipelines analyzing different brain areas and diseases.

This online platform is free for non-commercial and non-medical purposes (research only).

We are looking for collaboration, please contact us with any feedback

To report an issue on the platform or the pipelines, please use this Bug Report page.

Please read this Tutorial before starting your journey on volBrain ;-)

How it works ?


First, you have to register as a new user, or log into the system if you are already registered.


Second, upload your compressed anonymized brain MRI data in NIFTI format.


Once your data is uploaded, volBrain will process your request as soon as possible and generate a report containing the results.

Get Results

Finally, after your job completion you will receive an e-mail with a PDF report containing your results.

How to cite us ?

First, please cite the paper describing the platform:

- J V. Manjon and . Coupe. volBrain: an online MRI brain volumetry system. Frontiers in Neuroinformatics, 10, 30, 2016. PDF

Second, please cite the paper(s) describing the pipeline that you used. See the descrition page of each pipeline to find the reference(s). For instance, for the DeepLesionBrain pipeline, you should cite:

-R. A. Kamraoui, V.-T. Ta, T. Tourdias, B. Mansencal, J. V. Manjon, P. Coupé. DeepLesionBrain: Towards a broader deep-learning generalization for multiple sclerosis lesion segmentation. Medical Image Analysis 76 (2022): 102312 PDF

-P. Coupé, B. Mansencal, M. Clément, R. Giraud, B. Denis de Senneville, V.-T Ta, V. Lepetit, J. V. Manjon. AssemblyNet: A large ensemble of CNNs for 3D Whole Brain MRI Segmentation. NeuroImage, 219, 117026, 2020 PDF

-J. V. Manjon, J. E. Romero, R. Vivo-Hernando, G. Rubio, F. Aparici, M. de La Iglesia-Vaya, T. Tourdias, P. Coupé. Blind MRI brain lesion inpainting using deep learning. SASHIMI workshop MICCAI 2020 PDF

-de Senneville, B.D., Manjon, J.V. and Coupé, P., 2020. RegQCNET: Deep quality control for image-to-template brain MRI affine registration. Physics in Medicine & Biology, 65(22), p.225022.PDF