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volBrain: An Online MRI Brain Volumetry System

The amount of medical image data produced in clinical and research settings is rapidly growing resulting in vast amount of data to analyze. Automatic and reliable quantitative analysis tools, including segmentation, allow to analyze brain development and to understand specific patterns of many neuro...

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Detalles Bibliográficos
Autores principales: Manjón, José V., Coupé, Pierrick
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4961698/
https://www.ncbi.nlm.nih.gov/pubmed/27512372
http://dx.doi.org/10.3389/fninf.2016.00030
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author Manjón, José V.
Coupé, Pierrick
author_facet Manjón, José V.
Coupé, Pierrick
author_sort Manjón, José V.
collection PubMed
description The amount of medical image data produced in clinical and research settings is rapidly growing resulting in vast amount of data to analyze. Automatic and reliable quantitative analysis tools, including segmentation, allow to analyze brain development and to understand specific patterns of many neurological diseases. This field has recently experienced many advances with successful techniques based on non-linear warping and label fusion. In this work we present a novel and fully automatic pipeline for volumetric brain analysis based on multi-atlas label fusion technology that is able to provide accurate volumetric information at different levels of detail in a short time. This method is available through the volBrain online web interface (http://volbrain.upv.es), which is publically and freely accessible to the scientific community. Our new framework has been compared with current state-of-the-art methods showing very competitive results.
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spelling pubmed-49616982016-08-10 volBrain: An Online MRI Brain Volumetry System Manjón, José V. Coupé, Pierrick Front Neuroinform Neuroscience The amount of medical image data produced in clinical and research settings is rapidly growing resulting in vast amount of data to analyze. Automatic and reliable quantitative analysis tools, including segmentation, allow to analyze brain development and to understand specific patterns of many neurological diseases. This field has recently experienced many advances with successful techniques based on non-linear warping and label fusion. In this work we present a novel and fully automatic pipeline for volumetric brain analysis based on multi-atlas label fusion technology that is able to provide accurate volumetric information at different levels of detail in a short time. This method is available through the volBrain online web interface (http://volbrain.upv.es), which is publically and freely accessible to the scientific community. Our new framework has been compared with current state-of-the-art methods showing very competitive results. Frontiers Media S.A. 2016-07-27 /pmc/articles/PMC4961698/ /pubmed/27512372 http://dx.doi.org/10.3389/fninf.2016.00030 Text en Copyright © 2016 Manjón and Coupé. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Manjón, José V.
Coupé, Pierrick
volBrain: An Online MRI Brain Volumetry System
title volBrain: An Online MRI Brain Volumetry System
title_full volBrain: An Online MRI Brain Volumetry System
title_fullStr volBrain: An Online MRI Brain Volumetry System
title_full_unstemmed volBrain: An Online MRI Brain Volumetry System
title_short volBrain: An Online MRI Brain Volumetry System
title_sort volbrain: an online mri brain volumetry system
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4961698/
https://www.ncbi.nlm.nih.gov/pubmed/27512372
http://dx.doi.org/10.3389/fninf.2016.00030
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