Advances in the use of deep learning for the analysis of magnetic resonance image in neuro-oncology

Machine Learning is entering a phase of maturity, but its medical applications still lag behind in terms of practical use. The field of oncological radiology (and neuro-oncology in particular) is at the forefront of these developments, now boosted by the success of Deep-Learning methods for the anal...

Descripción completa

Detalles Bibliográficos
Autores: Pitarch i Abaigar, Carla|||0000-0002-6015-244X, Ungan, Gülnur, Julia Sape, Margarida, Vellido Alcacena, Alfredo|||0000-0002-9843-1911
Tipo de recurso: artículo
Fecha de publicación:2024
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/402636
Acceso en línea:https://hdl.handle.net/2117/402636
https://dx.doi.org/10.3390/cancers16020300
Access Level:acceso abierto
Palabra clave:Machine learning
Magnetic resonance imaging
Brain --Tumors
Neuro-oncology
Radiology
Deep learning
Data analysis pipeline
Ultra-low field magnetic resonance imaging
Aprenentatge automàtic
Imatgeria per ressonància magnètica
Cervell -- Tumors
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
id ES_b40dcff37bb61ef7ad1fe3febfb6338b
oai_identifier_str oai:upcommons.upc.edu:2117/402636
network_acronym_str ES
network_name_str España
repository_id_str
spelling Advances in the use of deep learning for the analysis of magnetic resonance image in neuro-oncologyPitarch i Abaigar, Carla|||0000-0002-6015-244XUngan, GülnurJulia Sape, MargaridaVellido Alcacena, Alfredo|||0000-0002-9843-1911Machine learningMagnetic resonance imagingBrain --TumorsNeuro-oncologyRadiologyDeep learningData analysis pipelineUltra-low field magnetic resonance imagingAprenentatge automàticImatgeria per ressonància magnèticaCervell -- TumorsÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàticMachine Learning is entering a phase of maturity, but its medical applications still lag behind in terms of practical use. The field of oncological radiology (and neuro-oncology in particular) is at the forefront of these developments, now boosted by the success of Deep-Learning methods for the analysis of medical images. This paper reviews in detail some of the most recent advances in the use of Deep Learning in this field, from the broader topic of the development of Machine-Learning-based analytical pipelines to specific instantiations of the use of Deep Learning in neuro-oncology; the latter including its use in the groundbreaking field of ultra-low field magnetic resonance imaging.This research was funded by H2020-EU.1.3.—EXCELLENT SCIENCE—Marie Skłodowska-Curie Actions, grant number H2020-MSCA-ITN-2018-813120; Proyectos de investigación en salud 2020, grant number PI20/00064. PID2019-104551RB-I00; Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN (http://www.ciber-bbn.es/en, accessed on 3 November 2023), CB06/01/0010), an initiative of the Instituto de Salud Carlos III (Spain) co-funded by EU Fondo Europeo de Desarrollo Regional (FEDER); Spanish Agencia Española de Investigación (AEI) PID2022-143299OB-I00 grant; XartecSalut 2021-XARDI-00021. Carla Pitarch is a fellow of Eurecat’s “Vicente López” Ph.D. grant program.Peer ReviewedMultidisciplinary Digital Publishing Institute (MDPI)20242024-01-1020242024-02-22journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/402636https://dx.doi.org/10.3390/cancers16020300reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)InglésengEuropean Commission http://doi.org/10.13039/100010661 Horizon 2020 Framework Programme 813120 INtegrating Magnetic Resonance SPectroscopy and Multimodal Imaging for Research and Education in MEDicineAgencia Estatal de Investigación http://doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 PID2019-104551RB-I00 APRENDIZAJE AUTOMATICO PARA LA MODELIZACION DE LA DINAMICA MOLECULAR DE LAS PROTEINAS GPCRAgencia Estatal de Investigación http://doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023 PID2022-143299OB-I00 APOYO A LA DECISION EN OFTALMOLOGIA BASADO EN MACHINE LEARNING Y APLICADO A IMAGENES MULTI-MODALES DE LA RETINAopen accesshttp://purl.org/coar/access_right/c_abf2Attribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/4026362026-05-27T15:37:01Z
dc.title.none.fl_str_mv Advances in the use of deep learning for the analysis of magnetic resonance image in neuro-oncology
title Advances in the use of deep learning for the analysis of magnetic resonance image in neuro-oncology
spellingShingle Advances in the use of deep learning for the analysis of magnetic resonance image in neuro-oncology
Pitarch i Abaigar, Carla|||0000-0002-6015-244X
Machine learning
Magnetic resonance imaging
Brain --Tumors
Neuro-oncology
Radiology
Deep learning
Data analysis pipeline
Ultra-low field magnetic resonance imaging
Aprenentatge automàtic
Imatgeria per ressonància magnètica
Cervell -- Tumors
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
title_short Advances in the use of deep learning for the analysis of magnetic resonance image in neuro-oncology
title_full Advances in the use of deep learning for the analysis of magnetic resonance image in neuro-oncology
title_fullStr Advances in the use of deep learning for the analysis of magnetic resonance image in neuro-oncology
title_full_unstemmed Advances in the use of deep learning for the analysis of magnetic resonance image in neuro-oncology
title_sort Advances in the use of deep learning for the analysis of magnetic resonance image in neuro-oncology
dc.creator.none.fl_str_mv Pitarch i Abaigar, Carla|||0000-0002-6015-244X
Ungan, Gülnur
Julia Sape, Margarida
Vellido Alcacena, Alfredo|||0000-0002-9843-1911
author Pitarch i Abaigar, Carla|||0000-0002-6015-244X
author_facet Pitarch i Abaigar, Carla|||0000-0002-6015-244X
Ungan, Gülnur
Julia Sape, Margarida
Vellido Alcacena, Alfredo|||0000-0002-9843-1911
author_role author
author2 Ungan, Gülnur
Julia Sape, Margarida
Vellido Alcacena, Alfredo|||0000-0002-9843-1911
author2_role author
author
author
dc.subject.none.fl_str_mv Machine learning
Magnetic resonance imaging
Brain --Tumors
Neuro-oncology
Radiology
Deep learning
Data analysis pipeline
Ultra-low field magnetic resonance imaging
Aprenentatge automàtic
Imatgeria per ressonància magnètica
Cervell -- Tumors
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
topic Machine learning
Magnetic resonance imaging
Brain --Tumors
Neuro-oncology
Radiology
Deep learning
Data analysis pipeline
Ultra-low field magnetic resonance imaging
Aprenentatge automàtic
Imatgeria per ressonància magnètica
Cervell -- Tumors
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
description Machine Learning is entering a phase of maturity, but its medical applications still lag behind in terms of practical use. The field of oncological radiology (and neuro-oncology in particular) is at the forefront of these developments, now boosted by the success of Deep-Learning methods for the analysis of medical images. This paper reviews in detail some of the most recent advances in the use of Deep Learning in this field, from the broader topic of the development of Machine-Learning-based analytical pipelines to specific instantiations of the use of Deep Learning in neuro-oncology; the latter including its use in the groundbreaking field of ultra-low field magnetic resonance imaging.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-01-10
2024
2024-02-22
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://hdl.handle.net/2117/402636
https://dx.doi.org/10.3390/cancers16020300
url https://hdl.handle.net/2117/402636
https://dx.doi.org/10.3390/cancers16020300
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv European Commission http://doi.org/10.13039/100010661 Horizon 2020 Framework Programme 813120 INtegrating Magnetic Resonance SPectroscopy and Multimodal Imaging for Research and Education in MEDicine
Agencia Estatal de Investigación http://doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 PID2019-104551RB-I00 APRENDIZAJE AUTOMATICO PARA LA MODELIZACION DE LA DINAMICA MOLECULAR DE LAS PROTEINAS GPCR
Agencia Estatal de Investigación http://doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023 PID2022-143299OB-I00 APOYO A LA DECISION EN OFTALMOLOGIA BASADO EN MACHINE LEARNING Y APLICADO A IMAGENES MULTI-MODALES DE LA RETINA
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution 4.0 International
http://creativecommons.org/licenses/by/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
Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute (MDPI)
publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute (MDPI)
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869417228660637696
score 15,300724

Similares en LA Referencia