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Automatic and reliable quantitative tools for MR brain image analysis are avery valuable resources for both clinical and research environments. In thelast years, this field has experienced many advances with successful techniquesbased on label fusion and more recently deep learning. However, few of themhave been specifically designed to provide a dense anatomical labelling atmultiscale level and to deal with brain anatomical alterations such as whitematter lesions. In this work, we present a fully automatic pipeline (vol2Brain)for whole brain segmentation and analysis which densely labels (N>100) thebrain while being robust to the presence of white matter lesions. This newpipeline is an evolution of our previous volBrain pipeline that extendssignificantly the number of regions that can be analyzed. Our proposed methodis based on a fast multiscale multi-atlas label fusion technology withsystematic error correction able to provide accurate volumetric information infew minutes. We have deployed our new pipeline within our platform volBrain(www.volbrain.upv.es) which has been already demonstrated to be an efficientand effective manner to share our technology with users worldwide

vol2Brain: A new online Pipeline for whole Brain MRI analysis