pBrain: A novel pipeline for Parkinson related brain structure segmentation

[EN] Parkinson is a very prevalent neurodegenerative disease impacting the life of millions of people worldwide. Although its cause remains unknown, its functional and structural analysis is fundamental to advance in the search of a cure or symptomatic treatment. The automatic segmentation of deep b...

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Authors: Manjón Herrera, José Vicente|||0000-0001-6640-927X, Vivó, Roberto|||0000-0002-0751-4114, Bertó, Alexa, Romero, José E., Lanuza, Enrique, Aparici-Robles, Fernando, Coupé, Pierrick
Format: article
Publication Date:2020
Country:España
Institution:Universitat Politècnica de València (UPV)
Repository:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Language:English
OAI Identifier:oai:riunet.upv.es:10251/176114
Online Access:https://riunet.upv.es/handle/10251/176114
Access Level:Open access
Keyword:FISICA APLICADA
LENGUAJES Y SISTEMAS INFORMATICOS
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network_acronym_str ES
network_name_str España
repository_id_str
spelling pBrain: A novel pipeline for Parkinson related brain structure segmentationManjón Herrera, José Vicente|||0000-0001-6640-927XVivó, Roberto|||0000-0002-0751-4114Bertó, AlexaRomero, José E.Lanuza, EnriqueAparici-Robles, FernandoCoupé, PierrickFISICA APLICADALENGUAJES Y SISTEMAS INFORMATICOS[EN] Parkinson is a very prevalent neurodegenerative disease impacting the life of millions of people worldwide. Although its cause remains unknown, its functional and structural analysis is fundamental to advance in the search of a cure or symptomatic treatment. The automatic segmentation of deep brain structures related to Parkinson's disease could be beneficial for the follow up and treatment planning. Unfortunately, there is not broadly available segmentation software to automatically measure Parkinson related structures. In this paper, we present a novel pipeline to segment three deep brain structures related to Parkinson's disease (substantia nigra, subthalamic nucleus and red nucleus). The proposed method is based on the multi-atlas label fusion technology that works on standard and high-resolution T2-weighted images. The proposed method also includes as post-processing a new neural network-based error correction step to minimize systematic segmentation errors. The proposed method has been compared to other state-of-the-art methods showing competitive results in terms of accuracy and execution time.The authors want to thank Dr. Mallar Chakravarty for making accessible the HR MRI data used in the proposed pipeline. This research was supported by the Spanish DPI2017-87743-R grant from the Ministerio de Economia, Industria y Competitividad of Spain. This work also 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. Moreover, we thank the Investments for the future Program IdEx Bordeaux (ANR-10-IDEX-03-02, HL-MRI Project), Cluster of excellence CPU and the CNRS.ElsevierDepartamento de Física AplicadaDepartamento de Sistemas Informáticos y ComputaciónInstituto Universitario de Tecnologías de la Información y ComunicacionesInstituto Universitario de Automática e Informática IndustrialEscuela Técnica Superior de Ingeniería InformáticaAgencia Estatal de InvestigaciónAgence Nationale de la Recherche, FranciaCentre National de la Recherche Scientifique, FranciaRepositorio Institucional de la Universitat Politècnica de València Riunet20202020-01-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://riunet.upv.es/handle/10251/176114reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)InglésengAgence Nationale de la Recherche, Francia https://doi.org/10.13039/501100001665 ANR-10-LABX-57Agence Nationale de la Recherche, Francia https://doi.org/10.13039/501100001665 ANR-10-IDEX-03-02Agence Nationale de la Recherche, Francia https://doi.org/10.13039/501100001665 ANR-18-CE45-0013Agencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 DPI2017-87743-R DESARROLLO DE UNA PLATAFORMA ONLINE PARA EL ANALISIS ANATOMICO DEL CEREBRO TOLERANTE A LA PRESENCIA DE ALTERACIONES PATOLOGICASopen accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/1761142026-06-13T07:49:27Z
dc.title.none.fl_str_mv pBrain: A novel pipeline for Parkinson related brain structure segmentation
title pBrain: A novel pipeline for Parkinson related brain structure segmentation
spellingShingle pBrain: A novel pipeline for Parkinson related brain structure segmentation
Manjón Herrera, José Vicente|||0000-0001-6640-927X
FISICA APLICADA
LENGUAJES Y SISTEMAS INFORMATICOS
title_short pBrain: A novel pipeline for Parkinson related brain structure segmentation
title_full pBrain: A novel pipeline for Parkinson related brain structure segmentation
title_fullStr pBrain: A novel pipeline for Parkinson related brain structure segmentation
title_full_unstemmed pBrain: A novel pipeline for Parkinson related brain structure segmentation
title_sort pBrain: A novel pipeline for Parkinson related brain structure segmentation
dc.creator.none.fl_str_mv Manjón Herrera, José Vicente|||0000-0001-6640-927X
Vivó, Roberto|||0000-0002-0751-4114
Bertó, Alexa
Romero, José E.
Lanuza, Enrique
Aparici-Robles, Fernando
Coupé, Pierrick
author Manjón Herrera, José Vicente|||0000-0001-6640-927X
author_facet Manjón Herrera, José Vicente|||0000-0001-6640-927X
Vivó, Roberto|||0000-0002-0751-4114
Bertó, Alexa
Romero, José E.
Lanuza, Enrique
Aparici-Robles, Fernando
Coupé, Pierrick
author_role author
author2 Vivó, Roberto|||0000-0002-0751-4114
Bertó, Alexa
Romero, José E.
Lanuza, Enrique
Aparici-Robles, Fernando
Coupé, Pierrick
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv Departamento de Física Aplicada
Departamento de Sistemas Informáticos y Computación
Instituto Universitario de Tecnologías de la Información y Comunicaciones
Instituto Universitario de Automática e Informática Industrial
Escuela Técnica Superior de Ingeniería Informática
Agencia Estatal de Investigación
Agence Nationale de la Recherche, Francia
Centre National de la Recherche Scientifique, Francia
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv FISICA APLICADA
LENGUAJES Y SISTEMAS INFORMATICOS
topic FISICA APLICADA
LENGUAJES Y SISTEMAS INFORMATICOS
description [EN] Parkinson is a very prevalent neurodegenerative disease impacting the life of millions of people worldwide. Although its cause remains unknown, its functional and structural analysis is fundamental to advance in the search of a cure or symptomatic treatment. The automatic segmentation of deep brain structures related to Parkinson's disease could be beneficial for the follow up and treatment planning. Unfortunately, there is not broadly available segmentation software to automatically measure Parkinson related structures. In this paper, we present a novel pipeline to segment three deep brain structures related to Parkinson's disease (substantia nigra, subthalamic nucleus and red nucleus). The proposed method is based on the multi-atlas label fusion technology that works on standard and high-resolution T2-weighted images. The proposed method also includes as post-processing a new neural network-based error correction step to minimize systematic segmentation errors. The proposed method has been compared to other state-of-the-art methods showing competitive results in terms of accuracy and execution time.
publishDate 2020
dc.date.none.fl_str_mv 2020
2020-01-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/176114
url https://riunet.upv.es/handle/10251/176114
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Agence Nationale de la Recherche, Francia https://doi.org/10.13039/501100001665 ANR-10-LABX-57
Agence Nationale de la Recherche, Francia https://doi.org/10.13039/501100001665 ANR-10-IDEX-03-02
Agence Nationale de la Recherche, Francia https://doi.org/10.13039/501100001665 ANR-18-CE45-0013
Agencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 DPI2017-87743-R DESARROLLO DE UNA PLATAFORMA ONLINE PARA EL ANALISIS ANATOMICO DEL CEREBRO TOLERANTE A LA PRESENCIA DE ALTERACIONES PATOLOGICAS
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300719

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