Use of computational realistic models for the cardiac ejection fraction calculation

Ejection fraction is one of the most useful clinical descriptors to determine the cardiac function of a subject. For this reason, obtaining the value of this descriptor is of vital importance and requires high precision. However, in the clinical routine, to generate the mentioned descriptor value, a...

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Autores:
Huérfano, Y
Vera, M
Vera, M I
Valbuena, O
Gelvez-Almeida, E
Salazar-Torres, J
Tipo de recurso:
Fecha de publicación:
2019
Institución:
Universidad Simón Bolívar
Repositorio:
Repositorio Digital USB
Idioma:
eng
OAI Identifier:
oai:bonga.unisimon.edu.co:20.500.12442/5099
Acceso en línea:
https://hdl.handle.net/20.500.12442/5099
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dc.title.eng.fl_str_mv Use of computational realistic models for the cardiac ejection fraction calculation
title Use of computational realistic models for the cardiac ejection fraction calculation
spellingShingle Use of computational realistic models for the cardiac ejection fraction calculation
title_short Use of computational realistic models for the cardiac ejection fraction calculation
title_full Use of computational realistic models for the cardiac ejection fraction calculation
title_fullStr Use of computational realistic models for the cardiac ejection fraction calculation
title_full_unstemmed Use of computational realistic models for the cardiac ejection fraction calculation
title_sort Use of computational realistic models for the cardiac ejection fraction calculation
dc.creator.fl_str_mv Huérfano, Y
Vera, M
Vera, M I
Valbuena, O
Gelvez-Almeida, E
Salazar-Torres, J
dc.contributor.author.none.fl_str_mv Huérfano, Y
Vera, M
Vera, M I
Valbuena, O
Gelvez-Almeida, E
Salazar-Torres, J
description Ejection fraction is one of the most useful clinical descriptors to determine the cardiac function of a subject. For this reason, obtaining the value of this descriptor is of vital importance and requires high precision. However, in the clinical routine, to generate the mentioned descriptor value, a geometric hypothesis is assumed, obtaining an approximate value for this fraction, usually by excess, and which is a dependent-operator. The aim of the present work is to propose the accurate calculation of the ejection fraction from realistic models, obtained computationally, of the cardiac chamber called right ventricle. Normally, the geometric hypothesis that makes this ventricle coincide with a pyramidal type geometric shape, is not usually, fulfilled in subjects affected by several cardiac pathologies, so as an alternative to this problem, the computational segmentation process is used to generate the morphology of the right ventricle and from it proceeds to obtain, accurately, the ejection fraction value. In this sense, an automatic strategy based on no-lineal filters, smart operator and region growing technique is propose in order to generate the right ventricle ejection fraction. The results are promising due we obtained an excellent correspondence between the manual segmentation and the automatic one generated by the realistic models.
publishDate 2019
dc.date.issued.none.fl_str_mv 2019
dc.date.accessioned.none.fl_str_mv 2020-04-14T04:02:05Z
dc.date.available.none.fl_str_mv 2020-04-14T04:02:05Z
dc.type.eng.fl_str_mv article
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dc.type.driver.eng.fl_str_mv article
dc.identifier.issn.none.fl_str_mv 17426596
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12442/5099
identifier_str_mv 17426596
url https://hdl.handle.net/20.500.12442/5099
dc.language.iso.eng.fl_str_mv eng
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dc.rights.*.fl_str_mv Attribution-NonCommercial-NoDerivatives 4.0 Internacional
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dc.format.mimetype.eng.fl_str_mv pdf
dc.publisher.eng.fl_str_mv IOP Publishing
dc.source.eng.fl_str_mv Journal of Physics: Conference Series
Vol. 1408 (2019)
institution Universidad Simón Bolívar
dc.source.uri.eng.fl_str_mv https://iopscience.iop.org/article/10.1088/1742-6596/1408/1/012003
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spelling Huérfano, Y001cc35e-75ac-48b8-9fd0-3c22464ff80fVera, M847eada8-99d3-4ff1-a613-ae3f62c30f9eVera, M I4c675edd-c7b6-4fee-87e2-feb90cfc363eValbuena, O4286f2e0-ce46-49ce-a106-bd00c21a76e9Gelvez-Almeida, E55062614-d175-4da1-834a-d7e54dcc92deSalazar-Torres, J40a2a6c9-3e39-4994-9b5a-1c6112bd80002020-04-14T04:02:05Z2020-04-14T04:02:05Z201917426596https://hdl.handle.net/20.500.12442/5099Ejection fraction is one of the most useful clinical descriptors to determine the cardiac function of a subject. For this reason, obtaining the value of this descriptor is of vital importance and requires high precision. However, in the clinical routine, to generate the mentioned descriptor value, a geometric hypothesis is assumed, obtaining an approximate value for this fraction, usually by excess, and which is a dependent-operator. The aim of the present work is to propose the accurate calculation of the ejection fraction from realistic models, obtained computationally, of the cardiac chamber called right ventricle. Normally, the geometric hypothesis that makes this ventricle coincide with a pyramidal type geometric shape, is not usually, fulfilled in subjects affected by several cardiac pathologies, so as an alternative to this problem, the computational segmentation process is used to generate the morphology of the right ventricle and from it proceeds to obtain, accurately, the ejection fraction value. In this sense, an automatic strategy based on no-lineal filters, smart operator and region growing technique is propose in order to generate the right ventricle ejection fraction. The results are promising due we obtained an excellent correspondence between the manual segmentation and the automatic one generated by the realistic models.pdfengIOP PublishingAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/http://purl.org/coar/access_right/c_abf2Journal of Physics: Conference SeriesVol. 1408 (2019)https://iopscience.iop.org/article/10.1088/1742-6596/1408/1/012003Use of computational realistic models for the cardiac ejection fraction calculationarticlearticlehttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_6501Guyton A, Hall J 2006 Medical physiology textbook (USA: W. B. Saunders)Frangi A, Niessen W, Viergever M 2001 Three-dimensional modeling for functional analysis of cardiac images: A review IEEE Transactions on Medical Imaging 20(1) 2Shapiro L, Stockman G 2001 Computer vision (New Jersey: Pearson)Haykin S 1999 Neural networks: A comprehensive foundation (New Jersey: Prentice Hall)Scholkopf B, Smola A 2002 Learning with kernels: Support vector machines, regularization, optimization, and beyond (USA: The MIT Press)Suykens J, Van Gestel T, De Brabanter J 2002 Least squares support vector machines (UK: World Scientific Publishing Co.)Vapnik V 1995 The nature of statistical learning theory (New York: Springer Verlag)Pratt W 2007 Digital image processing (New York: John Wiley & Sons Inc)Huérfano Y, Vera M, Mar A, Bravo A 2019 Integrating a gradient–based difference operator with machine learning techniques in right heart segmentation J. Phys. Conf. Ser. 1160 012003González R and Woods R 2001 Digital image processing (New Jersey: Prentice Hall)Vera M, Medina R, Del Mar A, Arellano J, Huérfano Y, Bravo A 2019 An automatic technique for left ventricle segmentation from msct cardiac volumes. J. Phys. Conf. Ser. 1160 012001Bravo A, Vera M, Garreau M, Medina R 2011 Three–dimensional segmentation of ventricular heart chambers from multi–slice computerized tomography: An hybrid approach Proc. Digital Information and Communication Technology and Its Applications (France: Springer) 166 287Dice L 1945 Measures of the amount of ecologic association between species Ecology 26(3) 29Arias V, Contreras J, Chacón J, Vera M, Huérfano Y, Graterol M, Wilches S, Rojas J, Garicano C, Chacín M, Bermúdez V 2015 Impresión 3D de estructuras cardiacas: Caso de innovación frugal en sector salud Latinoamericana de Hipertensión 10(4) 91ORIGINALPDF.pdfPDF.pdfPDFapplication/pdf916845https://bonga.unisimon.edu.co/bitstreams/d9e7453b-f1ba-4b29-b851-112bb7adc81a/download4c7ae86feccf5693754760b68828dd46MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://bonga.unisimon.edu.co/bitstreams/4c1d52eb-95f6-4501-ac36-cbae1c1f26ac/download4460e5956bc1d1639be9ae6146a50347MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-8381https://bonga.unisimon.edu.co/bitstreams/27d63a8a-bec6-4616-a5b2-7c51aaadc2c5/download733bec43a0bf5ade4d97db708e29b185MD53TEXTUse_CRM_Cardiac_ejection_fraction.pdf.txtUse_CRM_Cardiac_ejection_fraction.pdf.txtExtracted texttext/plain16330https://bonga.unisimon.edu.co/bitstreams/b0ea5c05-3550-47c9-99b1-b3c23aa1dc4c/download51d837319406d3a1d0332a6ba9da7d8aMD54PDF.pdf.txtPDF.pdf.txtExtracted texttext/plain16871https://bonga.unisimon.edu.co/bitstreams/4afab659-db17-487c-a671-3747b7c47d83/download05e233323dd21a4d57d8f37dee85ecd8MD56THUMBNAILUse_CRM_Cardiac_ejection_fraction.pdf.jpgUse_CRM_Cardiac_ejection_fraction.pdf.jpgGenerated Thumbnailimage/jpeg1277https://bonga.unisimon.edu.co/bitstreams/c5987ba7-b85a-49d5-9735-fc5008a53b9b/download1ade38ec7c0e302cecb4e4f7ea2dc0f0MD55PDF.pdf.jpgPDF.pdf.jpgGenerated Thumbnailimage/jpeg3318https://bonga.unisimon.edu.co/bitstreams/d90f7aae-0ebb-413d-9504-19c01864b223/downloada4e083c980b5c8865ac74b98706b87bfMD5720.500.12442/5099oai:bonga.unisimon.edu.co:20.500.12442/50992024-08-14 21:54:37.226http://creativecommons.org/licenses/by-nc-nd/4.0/Attribution-NonCommercial-NoDerivatives 4.0 Internacionalopen.accesshttps://bonga.unisimon.edu.coRepositorio Digital Universidad Simón Bolívarrepositorio.digital@unisimon.edu.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