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MRI white matter lesion segmentation using an ensemble of neural networks and overcomplete patch-based voting

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MRI white matter lesion segmentation using an ensemble of neural networks and overcomplete patch-based voting

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dc.contributor.author Manjón Herrera, José Vicente es_ES
dc.contributor.author Coupe, Pierrick es_ES
dc.contributor.author Raniga, Parnesh es_ES
dc.contributor.author Xia, Ying es_ES
dc.contributor.author Desmond, Patricia es_ES
dc.contributor.author Fripp, Jurgen es_ES
dc.contributor.author Salvado, Olivier es_ES
dc.date.accessioned 2020-07-07T03:33:34Z
dc.date.available 2020-07-07T03:33:34Z
dc.date.issued 2018-11 es_ES
dc.identifier.issn 0895-6111 es_ES
dc.identifier.uri http://hdl.handle.net/10251/147544
dc.description.abstract [EN] Accurate quantification of white matter hyperintensities (WMH) from Magnetic Resonance Imaging (MRI) is a valuable tool for the analysis of normal brain ageing or neurodegeneration. Reliable automatic extraction of WMH lesions is challenging due to their heterogeneous spatial occurrence, their small size and their diffuse nature. In this paper, we present an automatic method to segment these lesions based on an ensemble of overcomplete patch-based neural networks. The proposed method successfully provides accurate and regular segmentations due to its overcomplete nature while minimizing the segmentation error by using a boosted ensemble of neural networks. The proposed method compared favourably to state of the art techniques using two different neurodegenerative datasets. (C) 2018 Elsevier Ltd. All rights reserved. es_ES
dc.description.sponsorship This research has been done thanks to the Australian distinguished visiting professor grant from the CSIRO (Commonwealth Scientific and Industrial Research Organisation) and the Spanish "Programa de apoyo a la investigacion y desarrollo (PAID-00-15)" of the Universidad Politecnica de Valencia. This research was partially supported by the Spanish grant TIN2013-43457-R from the Ministerio de Economia y competitividad. This study has been carried out also with support from the French State, managed by the French National Research Ageny in the frame of the Investments for the future Program IdEx Bordeaux (ANR-10-IDEX-03-02, HL-MRI Project), Cluster of excellence CPU and TRAIL (HR-DTI ANR-10-LABX-57) and the CNRS multidisciplinary project Defi imag'In. Some of the data used in this work was collected by the AIBL study group. Funding for the AIBL study is provided by the CSIRO Flagship Collaboration Fund and the Science and Industry Endowment Fund (SIEF) in partnership with Edith Cowan University (ECU), Mental Health Research Institute (MHRI), Alzheimer's Australia (AA), National Ageing Research Institute (NARI), Austin Health, Macquarie University, CogState Ltd, Hollywood Private Hospital, and Sir Charles Gairdner Hospital. es_ES
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation UPV/PAID-00-15 es_ES
dc.relation ANR/ANR-10-LABX-57 es_ES
dc.relation ANR/ANR-10-IDEX-03-02 es_ES
dc.relation info:eu-repo/grantAgreement/MINECO//TIN2013-43457-R/ES/CARACTERIZACION DE FIRMAS BIOLOGICAS DE GLIOBLASTOMAS MEDIANTE MODELOS NO-SUPERVISADOS DE PREDICCION ESTRUCTURADA BASADOS EN BIOMARCADORES DE IMAGEN/ es_ES
dc.relation.ispartof Computerized Medical Imaging and Graphics es_ES
dc.rights Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) es_ES
dc.subject Lesion segmentation es_ES
dc.subject MRI es_ES
dc.subject Brain es_ES
dc.subject Patch-Based es_ES
dc.subject Neural network es_ES
dc.subject Ensemble es_ES
dc.subject.classification FISICA APLICADA es_ES
dc.title MRI white matter lesion segmentation using an ensemble of neural networks and overcomplete patch-based voting es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.compmedimag.2018.05.001 es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Física Aplicada - Departament de Física Aplicada es_ES
dc.description.bibliographicCitation Manjón Herrera, JV.; Coupe, P.; Raniga, P.; Xia, Y.; Desmond, P.; Fripp, J.; Salvado, O. (2018). MRI white matter lesion segmentation using an ensemble of neural networks and overcomplete patch-based voting. Computerized Medical Imaging and Graphics. 69:43-51. https://doi.org/10.1016/j.compmedimag.2018.05.001 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1016/j.compmedimag.2018.05.001 es_ES
dc.description.upvformatpinicio 43 es_ES
dc.description.upvformatpfin 51 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 69 es_ES
dc.relation.pasarela S\384863 es_ES
dc.contributor.funder Cogstate Ltd es_ES
dc.contributor.funder Macquarie University es_ES
dc.contributor.funder Alzheimer's Australia es_ES
dc.contributor.funder Edith Cowan University es_ES
dc.contributor.funder Ministerio de Economía y Empresa es_ES
dc.contributor.funder Universitat Politècnica de València es_ES
dc.contributor.funder Sir Charles Gairdner Hospital, Australia es_ES
dc.contributor.funder Agence Nationale de la Recherche, Francia es_ES
dc.contributor.funder Mental Health Research Institute, Australia es_ES
dc.contributor.funder National Ageing Research Institute, Australia es_ES
dc.contributor.funder Hollywood Private Hospital Research Foundation es_ES
dc.contributor.funder Science and Industry Endowment Fund, Australia es_ES
dc.contributor.funder Centre National de la Recherche Scientifique, Francia es_ES
dc.contributor.funder Commonwealth Scientific and Industrial Research Organisation es_ES


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