Convex non-negative matrix factorization for brain tumor delimitation from MRSI data

Background: Pattern Recognition techniques can provide invaluable insights in the field of neuro-oncology. This is because the clinical analysis of brain tumors requires the use of non-invasive methods that generate complex data in electronic format. Magnetic Resonance (MR), in the modalities of spe...

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Autores: Ortega-Martorell, Sandra|||0000-0001-9927-3209, Lisboa, P. J. G., Vellido, Alfredo|||0000-0002-9843-1911, Simoes, Rui Vasco|||0000-0001-7574-4723, Pumarola i Batlle, Martí|||0000-0002-0935-7941, Julià Sapé, Ma. Margarita|||0000-0002-3316-9027, Arús i Caraltó, Carles|||0000-0003-2510-2671
Tipo de recurso: artículo
Fecha de publicación:2012
País:España
Institución:Universitat Autònoma de Barcelona
Repositorio:Dipòsit Digital de Documents de la UAB
Idioma:inglés
OAI Identifier:oai:ddd.uab.cat:108789
Acceso en línea:https://ddd.uab.cat/record/108789
https://dx.doi.org/urn:doi:10.1371/journal.pone.0047824
Access Level:acceso abierto
Palabra clave:Cervell
Tumores cerebrales
Brain tumours
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spelling Convex non-negative matrix factorization for brain tumor delimitation from MRSI dataOrtega-Martorell, Sandra|||0000-0001-9927-3209Lisboa, P. J. G.Vellido, Alfredo|||0000-0002-9843-1911Simoes, Rui Vasco|||0000-0001-7574-4723Pumarola i Batlle, Martí|||0000-0002-0935-7941Julià Sapé, Ma. Margarita|||0000-0002-3316-9027Arús i Caraltó, Carles|||0000-0003-2510-2671CervellTumores cerebralesBrain tumoursBackground: Pattern Recognition techniques can provide invaluable insights in the field of neuro-oncology. This is because the clinical analysis of brain tumors requires the use of non-invasive methods that generate complex data in electronic format. Magnetic Resonance (MR), in the modalities of spectroscopy (MRS) and spectroscopic imaging (MRSI), has been widely applied to this purpose. The heterogeneity of the tissue in the brain volumes analyzed by MR remains a challenge in terms of pathological area delimitation. Methodology/Principal Findings: A pre-clinical study was carried out using seven brain tumor-bearing mice. Imaging and spectroscopy information was acquired from the brain tissue. A methodology is proposed to extract tissue type-specific sources from these signals by applying Convex Non-negative Matrix Factorization (Convex-NMF). Its suitability for the delimitation of pathological brain area from MRSI is experimentally confirmed by comparing the images obtained with its application to selected target regions, and to the gold standard of registered histopathology data. The former showed good accuracy for the solid tumor region (proliferation index (PI)>30%). The latter yielded (i) high sensitivity and specificity in most cases, (ii) acquisition conditions for safe thresholds in tumor and non-tumor regions (PI>30% for solid tumoral region; ≤5% for non-tumor), and (iii) fairly good results when borderline pixels were considered. Conclusions/Significance: The unsupervised nature of Convex-NMF, which does not use prior information regarding the tumor area for its delimitation, places this approach one step ahead of classical label-requiring supervised methods for discrimination between tissue types, minimizing the negative effect of using mislabeled voxels. Convex-NMF also relaxes the non-negativity constraints on the observed data, which allows for a natural representation of the MRSI signal. This should help radiologists to accurately tackle one of the main sources of uncertainty in the clinical management of brain tumors, which is the difficulty of appropriately delimiting the pathological area. 22012-01-0120122012-01-01Articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://ddd.uab.cat/record/108789https://dx.doi.org/urn:doi:10.1371/journal.pone.0047824reponame:Dipòsit Digital de Documents de la UABinstname:Universitat Autònoma de BarcelonaInglésengopen accesshttp://purl.org/coar/access_right/c_abf2Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.https://creativecommons.org/licenses/by/2.0/info:eu-repo/semantics/openAccessoai:ddd.uab.cat:1087892026-06-06T12:50:31Z
dc.title.none.fl_str_mv Convex non-negative matrix factorization for brain tumor delimitation from MRSI data
title Convex non-negative matrix factorization for brain tumor delimitation from MRSI data
spellingShingle Convex non-negative matrix factorization for brain tumor delimitation from MRSI data
Ortega-Martorell, Sandra|||0000-0001-9927-3209
Cervell
Tumores cerebrales
Brain tumours
title_short Convex non-negative matrix factorization for brain tumor delimitation from MRSI data
title_full Convex non-negative matrix factorization for brain tumor delimitation from MRSI data
title_fullStr Convex non-negative matrix factorization for brain tumor delimitation from MRSI data
title_full_unstemmed Convex non-negative matrix factorization for brain tumor delimitation from MRSI data
title_sort Convex non-negative matrix factorization for brain tumor delimitation from MRSI data
dc.creator.none.fl_str_mv Ortega-Martorell, Sandra|||0000-0001-9927-3209
Lisboa, P. J. G.
Vellido, Alfredo|||0000-0002-9843-1911
Simoes, Rui Vasco|||0000-0001-7574-4723
Pumarola i Batlle, Martí|||0000-0002-0935-7941
Julià Sapé, Ma. Margarita|||0000-0002-3316-9027
Arús i Caraltó, Carles|||0000-0003-2510-2671
author Ortega-Martorell, Sandra|||0000-0001-9927-3209
author_facet Ortega-Martorell, Sandra|||0000-0001-9927-3209
Lisboa, P. J. G.
Vellido, Alfredo|||0000-0002-9843-1911
Simoes, Rui Vasco|||0000-0001-7574-4723
Pumarola i Batlle, Martí|||0000-0002-0935-7941
Julià Sapé, Ma. Margarita|||0000-0002-3316-9027
Arús i Caraltó, Carles|||0000-0003-2510-2671
author_role author
author2 Lisboa, P. J. G.
Vellido, Alfredo|||0000-0002-9843-1911
Simoes, Rui Vasco|||0000-0001-7574-4723
Pumarola i Batlle, Martí|||0000-0002-0935-7941
Julià Sapé, Ma. Margarita|||0000-0002-3316-9027
Arús i Caraltó, Carles|||0000-0003-2510-2671
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv Cervell
Tumores cerebrales
Brain tumours
topic Cervell
Tumores cerebrales
Brain tumours
description Background: Pattern Recognition techniques can provide invaluable insights in the field of neuro-oncology. This is because the clinical analysis of brain tumors requires the use of non-invasive methods that generate complex data in electronic format. Magnetic Resonance (MR), in the modalities of spectroscopy (MRS) and spectroscopic imaging (MRSI), has been widely applied to this purpose. The heterogeneity of the tissue in the brain volumes analyzed by MR remains a challenge in terms of pathological area delimitation. Methodology/Principal Findings: A pre-clinical study was carried out using seven brain tumor-bearing mice. Imaging and spectroscopy information was acquired from the brain tissue. A methodology is proposed to extract tissue type-specific sources from these signals by applying Convex Non-negative Matrix Factorization (Convex-NMF). Its suitability for the delimitation of pathological brain area from MRSI is experimentally confirmed by comparing the images obtained with its application to selected target regions, and to the gold standard of registered histopathology data. The former showed good accuracy for the solid tumor region (proliferation index (PI)>30%). The latter yielded (i) high sensitivity and specificity in most cases, (ii) acquisition conditions for safe thresholds in tumor and non-tumor regions (PI>30% for solid tumoral region; ≤5% for non-tumor), and (iii) fairly good results when borderline pixels were considered. Conclusions/Significance: The unsupervised nature of Convex-NMF, which does not use prior information regarding the tumor area for its delimitation, places this approach one step ahead of classical label-requiring supervised methods for discrimination between tissue types, minimizing the negative effect of using mislabeled voxels. Convex-NMF also relaxes the non-negativity constraints on the observed data, which allows for a natural representation of the MRSI signal. This should help radiologists to accurately tackle one of the main sources of uncertainty in the clinical management of brain tumors, which is the difficulty of appropriately delimiting the pathological area.
publishDate 2012
dc.date.none.fl_str_mv 2
2012-01-01
2012
2012-01-01
dc.type.none.fl_str_mv 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://ddd.uab.cat/record/108789
https://dx.doi.org/urn:doi:10.1371/journal.pone.0047824
url https://ddd.uab.cat/record/108789
https://dx.doi.org/urn:doi:10.1371/journal.pone.0047824
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
https://creativecommons.org/licenses/by/2.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
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eu_rights_str_mv openAccess
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dc.source.none.fl_str_mv reponame:Dipòsit Digital de Documents de la UAB
instname:Universitat Autònoma de Barcelona
instname_str Universitat Autònoma de Barcelona
reponame_str Dipòsit Digital de Documents de la UAB
collection Dipòsit Digital de Documents de la UAB
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