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dc.contributor.author | Manjón Herrera, José Vicente![]() |
es_ES |
dc.contributor.author | Romero, José E.![]() |
es_ES |
dc.contributor.author | Vivó, Roberto![]() |
es_ES |
dc.contributor.author | Rubio Navarro, Gregorio![]() |
es_ES |
dc.contributor.author | Aparici, Fernando![]() |
es_ES |
dc.contributor.author | de la Iglesia-Vaya, Mariam![]() |
es_ES |
dc.contributor.author | Coupé, Pierrick![]() |
es_ES |
dc.date.accessioned | 2023-07-05T18:01:12Z | |
dc.date.available | 2023-07-05T18:01:12Z | |
dc.date.issued | 2022-05-24 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/194700 | |
dc.description.abstract | [EN] Automatic and reliable quantitative tools for MR brain image analysis are a very valuable resource for both clinical and research environments. In the past few years, this field has experienced many advances with successful techniques based on label fusion and more recently deep learning. However, few of them have been specifically designed to provide a dense anatomical labeling at the multiscale level and to deal with brain anatomical alterations such as white matter lesions (WML). In this work, we present a fully automatic pipeline (vol2Brain) for whole brain segmentation and analysis, which densely labels (N > 100) the brain while being robust to the presence of WML. This new pipeline is an evolution of our previous volBrain pipeline that extends significantly the number of regions that can be analyzed. Our proposed method is based on a fast and multiscale multi-atlas label fusion technology with systematic error correction able to provide accurate volumetric information in a few minutes. We have deployed our new pipeline within our platform volBrain (), which has been already demonstrated to be an efficient and effective way to share our technology with the users worldwide. | es_ES |
dc.description.sponsorship | This research was supported by the Spanish DPI2017-87743-R grant from the Ministerio de Economia, Industria y Competitividad of Spain. This work was benefited from the support of the project DeepvolBrain of the French National Research Agency (ANR-18-CE45-0013). This study was achieved within the context of the Laboratory of Excellence TRAIL ANR-10-LABX-57 for the BigDataBrain project. | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | Frontiers Media SA | es_ES |
dc.relation.ispartof | Frontiers in Neuroinformatics | es_ES |
dc.rights | Reconocimiento (by) | es_ES |
dc.subject | Segmentation | es_ES |
dc.subject | Brain | es_ES |
dc.subject | Analysis | es_ES |
dc.subject | MRI | es_ES |
dc.subject | Cloud | es_ES |
dc.subject.classification | FISICA APLICADA | es_ES |
dc.subject.classification | MATEMATICA APLICADA | es_ES |
dc.subject.classification | LENGUAJES Y SISTEMAS INFORMATICOS | es_ES |
dc.title | vol2Brain: A New Online Pipeline for Whole Brain MRI Analysis | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.3389/fninf.2022.862805 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/DPI2017-87743-R/ES/DESARROLLO DE UNA PLATAFORMA ONLINE PARA EL ANALISIS ANATOMICO DEL CEREBRO TOLERANTE A LA PRESENCIA DE ALTERACIONES PATOLOGICAS/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/ANR//ANR-18-CE45-0013/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/ANR//ANR-10-LABX-57/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Escola Tècnica Superior d'Enginyeria Informàtica | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Escuela Técnica Superior de Ingenieros Industriales - Escola Tècnica Superior d'Enginyers Industrials | es_ES |
dc.description.bibliographicCitation | Manjón Herrera, JV.; Romero, JE.; Vivó, R.; Rubio Navarro, G.; Aparici, F.; De La Iglesia-Vaya, M.; Coupé, P. (2022). vol2Brain: A New Online Pipeline for Whole Brain MRI Analysis. Frontiers in Neuroinformatics. 16:1-11. https://doi.org/10.3389/fninf.2022.862805 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.3389/fninf.2022.862805 | es_ES |
dc.description.upvformatpinicio | 1 | es_ES |
dc.description.upvformatpfin | 11 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 16 | es_ES |
dc.identifier.eissn | 1662-5196 | es_ES |
dc.identifier.pmid | 35685943 | es_ES |
dc.identifier.pmcid | PMC9171328 | es_ES |
dc.relation.pasarela | S\484090 | es_ES |
dc.contributor.funder | Agencia Estatal de Investigación | es_ES |
dc.contributor.funder | Agence Nationale de la Recherche, Francia | es_ES |
dc.contributor.funder | Universitat Politècnica de València | es_ES |
upv.costeAPC | 3570 | es_ES |