Mission
The volBrain open platform is an AI-powered MRI brain analysis 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 structures and diseases.
This online platform is free for non-commercial and non-medical purposes (research only).
To share our limited resources with as many of you as possible, each user can submit 10 jobs per day.
Thank you in advance for not creating multiple accounts to exceed this limit.
We are looking for collaboration, please contact us with any feedback team@volbrain.net
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.
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
News
A new pipeline named BrainStructureAges (BSA) is now available.
Based on AI, it provides age estimation and volume for each brain structure using a T1w MR image.
A new differential diagnosis pipeline named AssemblyNet-AD-FTD is now available.
Based on AI, it provides differential diagnosis between Alzheimer's Disease and Frontotemporal Dementia.
A new white matter lesion segmentation pipeline named deeplesionBrain is now available.
Based on AI, it provides white matter lesion segmentation from T1 and FLAIR images.
Welcome to the new platform volBrain.
We're back online with a novel platform offering new AI-based services.
