A Probabilistic Atlas of Diffuse WHO Grade II Glioma Locations in the Brain.

Diffuse WHO grade II gliomas are diffusively infiltrative brain tumors characterized by an unavoidable anaplastic transformation. Their management is strongly dependent on their location in the brain due to interactions with functional regions and potential differences in molecular biology. In this...
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Autor*in:	Sarah Parisot [verfasserIn] Amélie Darlix [verfasserIn] Cédric Baumann [verfasserIn] Sonia Zouaoui [verfasserIn] Yordanka Yordanova [verfasserIn] Marie Blonski [verfasserIn] Valérie Rigau [verfasserIn] Stéphane Chemouny [verfasserIn] Luc Taillandier [verfasserIn] Luc Bauchet [verfasserIn] Hugues Duffau [verfasserIn] Nikos Paragios [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2016

Übergeordnetes Werk:	In: PLoS ONE - Public Library of Science (PLoS), 2007, 11(2016), 1, p e0144200
Übergeordnetes Werk:	volume:11 ; year:2016 ; number:1, p e0144200

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1371/journal.pone.0144200

Katalog-ID:	DOAJ004170342

Internformat


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allfields	10.1371/journal.pone.0144200 doi (DE-627)DOAJ004170342 (DE-599)DOAJ621ecc999b1842228cb31def9713fe11 DE-627 ger DE-627 rakwb eng Sarah Parisot verfasserin aut A Probabilistic Atlas of Diffuse WHO Grade II Glioma Locations in the Brain. 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Diffuse WHO grade II gliomas are diffusively infiltrative brain tumors characterized by an unavoidable anaplastic transformation. Their management is strongly dependent on their location in the brain due to interactions with functional regions and potential differences in molecular biology. In this paper, we present the construction of a probabilistic atlas mapping the preferential locations of diffuse WHO grade II gliomas in the brain. This is carried out through a sparse graph whose nodes correspond to clusters of tumors clustered together based on their spatial proximity. The interest of such an atlas is illustrated via two applications. The first one correlates tumor location with the patient's age via a statistical analysis, highlighting the interest of the atlas for studying the origins and behavior of the tumors. The second exploits the fact that the tumors have preferential locations for automatic segmentation. Through a coupled decomposed Markov Random Field model, the atlas guides the segmentation process, and characterizes which preferential location the tumor belongs to and consequently which behavior it could be associated to. Leave-one-out cross validation experiments on a large database highlight the robustness of the graph, and yield promising segmentation results. Medicine R Science Q Amélie Darlix verfasserin aut Cédric Baumann verfasserin aut Sonia Zouaoui verfasserin aut Yordanka Yordanova verfasserin aut Marie Blonski verfasserin aut Valérie Rigau verfasserin aut Stéphane Chemouny verfasserin aut Luc Taillandier verfasserin aut Luc Bauchet verfasserin aut Hugues Duffau verfasserin aut Nikos Paragios verfasserin aut In PLoS ONE Public Library of Science (PLoS), 2007 11(2016), 1, p e0144200 (DE-627)523574592 (DE-600)2267670-3 19326203 nnns volume:11 year:2016 number:1, p e0144200 https://doi.org/10.1371/journal.pone.0144200 kostenfrei https://doaj.org/article/621ecc999b1842228cb31def9713fe11 kostenfrei http://europepmc.org/articles/PMC4709135?pdf=render kostenfrei https://doaj.org/toc/1932-6203 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_34 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_235 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2016 1, p e0144200
spelling	10.1371/journal.pone.0144200 doi (DE-627)DOAJ004170342 (DE-599)DOAJ621ecc999b1842228cb31def9713fe11 DE-627 ger DE-627 rakwb eng Sarah Parisot verfasserin aut A Probabilistic Atlas of Diffuse WHO Grade II Glioma Locations in the Brain. 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Diffuse WHO grade II gliomas are diffusively infiltrative brain tumors characterized by an unavoidable anaplastic transformation. Their management is strongly dependent on their location in the brain due to interactions with functional regions and potential differences in molecular biology. In this paper, we present the construction of a probabilistic atlas mapping the preferential locations of diffuse WHO grade II gliomas in the brain. This is carried out through a sparse graph whose nodes correspond to clusters of tumors clustered together based on their spatial proximity. The interest of such an atlas is illustrated via two applications. The first one correlates tumor location with the patient's age via a statistical analysis, highlighting the interest of the atlas for studying the origins and behavior of the tumors. The second exploits the fact that the tumors have preferential locations for automatic segmentation. Through a coupled decomposed Markov Random Field model, the atlas guides the segmentation process, and characterizes which preferential location the tumor belongs to and consequently which behavior it could be associated to. Leave-one-out cross validation experiments on a large database highlight the robustness of the graph, and yield promising segmentation results. Medicine R Science Q Amélie Darlix verfasserin aut Cédric Baumann verfasserin aut Sonia Zouaoui verfasserin aut Yordanka Yordanova verfasserin aut Marie Blonski verfasserin aut Valérie Rigau verfasserin aut Stéphane Chemouny verfasserin aut Luc Taillandier verfasserin aut Luc Bauchet verfasserin aut Hugues Duffau verfasserin aut Nikos Paragios verfasserin aut In PLoS ONE Public Library of Science (PLoS), 2007 11(2016), 1, p e0144200 (DE-627)523574592 (DE-600)2267670-3 19326203 nnns volume:11 year:2016 number:1, p e0144200 https://doi.org/10.1371/journal.pone.0144200 kostenfrei https://doaj.org/article/621ecc999b1842228cb31def9713fe11 kostenfrei http://europepmc.org/articles/PMC4709135?pdf=render kostenfrei https://doaj.org/toc/1932-6203 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_34 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_235 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2016 1, p e0144200
allfields_unstemmed	10.1371/journal.pone.0144200 doi (DE-627)DOAJ004170342 (DE-599)DOAJ621ecc999b1842228cb31def9713fe11 DE-627 ger DE-627 rakwb eng Sarah Parisot verfasserin aut A Probabilistic Atlas of Diffuse WHO Grade II Glioma Locations in the Brain. 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Diffuse WHO grade II gliomas are diffusively infiltrative brain tumors characterized by an unavoidable anaplastic transformation. Their management is strongly dependent on their location in the brain due to interactions with functional regions and potential differences in molecular biology. In this paper, we present the construction of a probabilistic atlas mapping the preferential locations of diffuse WHO grade II gliomas in the brain. This is carried out through a sparse graph whose nodes correspond to clusters of tumors clustered together based on their spatial proximity. The interest of such an atlas is illustrated via two applications. The first one correlates tumor location with the patient's age via a statistical analysis, highlighting the interest of the atlas for studying the origins and behavior of the tumors. The second exploits the fact that the tumors have preferential locations for automatic segmentation. Through a coupled decomposed Markov Random Field model, the atlas guides the segmentation process, and characterizes which preferential location the tumor belongs to and consequently which behavior it could be associated to. Leave-one-out cross validation experiments on a large database highlight the robustness of the graph, and yield promising segmentation results. Medicine R Science Q Amélie Darlix verfasserin aut Cédric Baumann verfasserin aut Sonia Zouaoui verfasserin aut Yordanka Yordanova verfasserin aut Marie Blonski verfasserin aut Valérie Rigau verfasserin aut Stéphane Chemouny verfasserin aut Luc Taillandier verfasserin aut Luc Bauchet verfasserin aut Hugues Duffau verfasserin aut Nikos Paragios verfasserin aut In PLoS ONE Public Library of Science (PLoS), 2007 11(2016), 1, p e0144200 (DE-627)523574592 (DE-600)2267670-3 19326203 nnns volume:11 year:2016 number:1, p e0144200 https://doi.org/10.1371/journal.pone.0144200 kostenfrei https://doaj.org/article/621ecc999b1842228cb31def9713fe11 kostenfrei http://europepmc.org/articles/PMC4709135?pdf=render kostenfrei https://doaj.org/toc/1932-6203 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_34 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_235 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2016 1, p e0144200
allfieldsGer	10.1371/journal.pone.0144200 doi (DE-627)DOAJ004170342 (DE-599)DOAJ621ecc999b1842228cb31def9713fe11 DE-627 ger DE-627 rakwb eng Sarah Parisot verfasserin aut A Probabilistic Atlas of Diffuse WHO Grade II Glioma Locations in the Brain. 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Diffuse WHO grade II gliomas are diffusively infiltrative brain tumors characterized by an unavoidable anaplastic transformation. Their management is strongly dependent on their location in the brain due to interactions with functional regions and potential differences in molecular biology. In this paper, we present the construction of a probabilistic atlas mapping the preferential locations of diffuse WHO grade II gliomas in the brain. This is carried out through a sparse graph whose nodes correspond to clusters of tumors clustered together based on their spatial proximity. The interest of such an atlas is illustrated via two applications. The first one correlates tumor location with the patient's age via a statistical analysis, highlighting the interest of the atlas for studying the origins and behavior of the tumors. The second exploits the fact that the tumors have preferential locations for automatic segmentation. Through a coupled decomposed Markov Random Field model, the atlas guides the segmentation process, and characterizes which preferential location the tumor belongs to and consequently which behavior it could be associated to. Leave-one-out cross validation experiments on a large database highlight the robustness of the graph, and yield promising segmentation results. Medicine R Science Q Amélie Darlix verfasserin aut Cédric Baumann verfasserin aut Sonia Zouaoui verfasserin aut Yordanka Yordanova verfasserin aut Marie Blonski verfasserin aut Valérie Rigau verfasserin aut Stéphane Chemouny verfasserin aut Luc Taillandier verfasserin aut Luc Bauchet verfasserin aut Hugues Duffau verfasserin aut Nikos Paragios verfasserin aut In PLoS ONE Public Library of Science (PLoS), 2007 11(2016), 1, p e0144200 (DE-627)523574592 (DE-600)2267670-3 19326203 nnns volume:11 year:2016 number:1, p e0144200 https://doi.org/10.1371/journal.pone.0144200 kostenfrei https://doaj.org/article/621ecc999b1842228cb31def9713fe11 kostenfrei http://europepmc.org/articles/PMC4709135?pdf=render kostenfrei https://doaj.org/toc/1932-6203 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_34 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_235 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2016 1, p e0144200
allfieldsSound	10.1371/journal.pone.0144200 doi (DE-627)DOAJ004170342 (DE-599)DOAJ621ecc999b1842228cb31def9713fe11 DE-627 ger DE-627 rakwb eng Sarah Parisot verfasserin aut A Probabilistic Atlas of Diffuse WHO Grade II Glioma Locations in the Brain. 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Diffuse WHO grade II gliomas are diffusively infiltrative brain tumors characterized by an unavoidable anaplastic transformation. Their management is strongly dependent on their location in the brain due to interactions with functional regions and potential differences in molecular biology. In this paper, we present the construction of a probabilistic atlas mapping the preferential locations of diffuse WHO grade II gliomas in the brain. This is carried out through a sparse graph whose nodes correspond to clusters of tumors clustered together based on their spatial proximity. The interest of such an atlas is illustrated via two applications. The first one correlates tumor location with the patient's age via a statistical analysis, highlighting the interest of the atlas for studying the origins and behavior of the tumors. The second exploits the fact that the tumors have preferential locations for automatic segmentation. Through a coupled decomposed Markov Random Field model, the atlas guides the segmentation process, and characterizes which preferential location the tumor belongs to and consequently which behavior it could be associated to. Leave-one-out cross validation experiments on a large database highlight the robustness of the graph, and yield promising segmentation results. Medicine R Science Q Amélie Darlix verfasserin aut Cédric Baumann verfasserin aut Sonia Zouaoui verfasserin aut Yordanka Yordanova verfasserin aut Marie Blonski verfasserin aut Valérie Rigau verfasserin aut Stéphane Chemouny verfasserin aut Luc Taillandier verfasserin aut Luc Bauchet verfasserin aut Hugues Duffau verfasserin aut Nikos Paragios verfasserin aut In PLoS ONE Public Library of Science (PLoS), 2007 11(2016), 1, p e0144200 (DE-627)523574592 (DE-600)2267670-3 19326203 nnns volume:11 year:2016 number:1, p e0144200 https://doi.org/10.1371/journal.pone.0144200 kostenfrei https://doaj.org/article/621ecc999b1842228cb31def9713fe11 kostenfrei http://europepmc.org/articles/PMC4709135?pdf=render kostenfrei https://doaj.org/toc/1932-6203 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_34 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_235 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2016 1, p e0144200
language	English
source	In PLoS ONE 11(2016), 1, p e0144200 volume:11 year:2016 number:1, p e0144200
sourceStr	In PLoS ONE 11(2016), 1, p e0144200 volume:11 year:2016 number:1, p e0144200
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Medicine R Science Q
isfreeaccess_bool	true
container_title	PLoS ONE
authorswithroles_txt_mv	Sarah Parisot @@aut@@ Amélie Darlix @@aut@@ Cédric Baumann @@aut@@ Sonia Zouaoui @@aut@@ Yordanka Yordanova @@aut@@ Marie Blonski @@aut@@ Valérie Rigau @@aut@@ Stéphane Chemouny @@aut@@ Luc Taillandier @@aut@@ Luc Bauchet @@aut@@ Hugues Duffau @@aut@@ Nikos Paragios @@aut@@
publishDateDaySort_date	2016-01-01T00:00:00Z
hierarchy_top_id	523574592
id	DOAJ004170342
language_de	englisch
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author	Sarah Parisot
spellingShingle	Sarah Parisot misc Medicine misc R misc Science misc Q A Probabilistic Atlas of Diffuse WHO Grade II Glioma Locations in the Brain.
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title	A Probabilistic Atlas of Diffuse WHO Grade II Glioma Locations in the Brain.
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title_sort	probabilistic atlas of diffuse who grade ii glioma locations in the brain
title_auth	A Probabilistic Atlas of Diffuse WHO Grade II Glioma Locations in the Brain.
abstract	Diffuse WHO grade II gliomas are diffusively infiltrative brain tumors characterized by an unavoidable anaplastic transformation. Their management is strongly dependent on their location in the brain due to interactions with functional regions and potential differences in molecular biology. In this paper, we present the construction of a probabilistic atlas mapping the preferential locations of diffuse WHO grade II gliomas in the brain. This is carried out through a sparse graph whose nodes correspond to clusters of tumors clustered together based on their spatial proximity. The interest of such an atlas is illustrated via two applications. The first one correlates tumor location with the patient's age via a statistical analysis, highlighting the interest of the atlas for studying the origins and behavior of the tumors. The second exploits the fact that the tumors have preferential locations for automatic segmentation. Through a coupled decomposed Markov Random Field model, the atlas guides the segmentation process, and characterizes which preferential location the tumor belongs to and consequently which behavior it could be associated to. Leave-one-out cross validation experiments on a large database highlight the robustness of the graph, and yield promising segmentation results.
abstractGer	Diffuse WHO grade II gliomas are diffusively infiltrative brain tumors characterized by an unavoidable anaplastic transformation. Their management is strongly dependent on their location in the brain due to interactions with functional regions and potential differences in molecular biology. In this paper, we present the construction of a probabilistic atlas mapping the preferential locations of diffuse WHO grade II gliomas in the brain. This is carried out through a sparse graph whose nodes correspond to clusters of tumors clustered together based on their spatial proximity. The interest of such an atlas is illustrated via two applications. The first one correlates tumor location with the patient's age via a statistical analysis, highlighting the interest of the atlas for studying the origins and behavior of the tumors. The second exploits the fact that the tumors have preferential locations for automatic segmentation. Through a coupled decomposed Markov Random Field model, the atlas guides the segmentation process, and characterizes which preferential location the tumor belongs to and consequently which behavior it could be associated to. Leave-one-out cross validation experiments on a large database highlight the robustness of the graph, and yield promising segmentation results.
abstract_unstemmed	Diffuse WHO grade II gliomas are diffusively infiltrative brain tumors characterized by an unavoidable anaplastic transformation. Their management is strongly dependent on their location in the brain due to interactions with functional regions and potential differences in molecular biology. In this paper, we present the construction of a probabilistic atlas mapping the preferential locations of diffuse WHO grade II gliomas in the brain. This is carried out through a sparse graph whose nodes correspond to clusters of tumors clustered together based on their spatial proximity. The interest of such an atlas is illustrated via two applications. The first one correlates tumor location with the patient's age via a statistical analysis, highlighting the interest of the atlas for studying the origins and behavior of the tumors. The second exploits the fact that the tumors have preferential locations for automatic segmentation. Through a coupled decomposed Markov Random Field model, the atlas guides the segmentation process, and characterizes which preferential location the tumor belongs to and consequently which behavior it could be associated to. Leave-one-out cross validation experiments on a large database highlight the robustness of the graph, and yield promising segmentation results.
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title_short	A Probabilistic Atlas of Diffuse WHO Grade II Glioma Locations in the Brain.
url	https://doi.org/10.1371/journal.pone.0144200 https://doaj.org/article/621ecc999b1842228cb31def9713fe11 http://europepmc.org/articles/PMC4709135?pdf=render https://doaj.org/toc/1932-6203
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score	7.3994074

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A Probabilistic Atlas of Diffuse WHO Grade II Glioma Locations in the Brain.

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