Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/58307
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dc.contributor.authorFabelo Gómez, Himar Antonioen_US
dc.contributor.authorHalicek, Martinen_US
dc.contributor.authorOrtega Sarmiento, Samuelen_US
dc.contributor.authorZbigniew Szolna,Adamen_US
dc.contributor.authorMorera Molina, Jesús Manuelen_US
dc.contributor.authorSarmiento Rodríguez, Robertoen_US
dc.contributor.authorMarrero Callicó, Gustavo Ivánen_US
dc.contributor.authorFei, Baoweien_US
dc.contributor.editorLinte, Cristian A.-
dc.contributor.editorFei, Baowei-
dc.date.accessioned2019-12-10T17:24:35Z-
dc.date.available2019-12-10T17:24:35Z-
dc.date.issued2019en_US
dc.identifier.isbn9781510625495-
dc.identifier.issn1605-7422en_US
dc.identifier.otherScopus-
dc.identifier.otherWoS-
dc.identifier.urihttp://hdl.handle.net/10553/58307-
dc.description.abstractBrain cancer surgery has the goal of performing an accurate resection of the tumor and preserving as much as possible the quality of life of the patient. There is a clinical need to develop non-invasive techniques that can provide reliable assistance for tumor resection in real-time during surgical procedures. Hyperspectral imaging (HSI) arises as a new, noninvasive and non-ionizing technique that can assist neurosurgeons during this difficult task. In this paper, we explore the use of deep learning (DL) techniques for processing hyperspectral (HS) images of in-vivo human brain tissue. We developed a surgical aid visualization system capable of offering guidance to the operating surgeon to achieve a successful and accurate tumor resection. The employed HS database is composed of 26 in-vivo hypercubes from 16 different human patients, among which 258,810 labelled pixels were used for evaluation. The proposed DL methods achieve an overall accuracy of 95% and 85% for binary and multiclass classifications, respectively. The proposed visualization system is able to generate a classification map that is formed by the combination of the DL map and an unsupervised clustering via a majority voting algorithm. This map can be adjusted by the operating surgeon to find the suitable configuration for the current situation during the surgical procedure.en_US
dc.languageengen_US
dc.relation.ispartofProgress in Biomedical Optics and Imaging - Proceedings of SPIEen_US
dc.sourceProgress in Biomedical Optics and Imaging - Proceedings of SPIE [ISSN 1605-7422], v. 10951, 1095110en_US
dc.subject3314 Tecnología médicaen_US
dc.subject.otherBrain Shiften_US
dc.subject.otherResectionen_US
dc.subject.otherExtenten_US
dc.subject.otherBrain Tumoren_US
dc.subject.otherCancer Surgeryen_US
dc.subject.otherHyperspectral Imagingen_US
dc.subject.otherIntraoperative Imagingen_US
dc.subject.otherDeep Learningen_US
dc.subject.otherSupervised Classificationen_US
dc.subject.otherConvolutional Neural Network (Cnn)en_US
dc.subject.otherClassifieren_US
dc.titleSurgical aid visualization system for glioblastoma tumor identification based on deep learning and in-vivo hyperspectral images of human patientsen_US
dc.typeinfo:eu-repo/semantics/articleen_US
dc.typeArticleen_US
dc.relation.conferenceMedical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling-
dc.identifier.doi10.1117/12.2512569en_US
dc.identifier.scopus85068937866-
dc.identifier.isi000483683500035-
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dc.contributor.orcid#NODATA#-
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dc.contributor.orcid#NODATA#-
dc.contributor.authorscopusid56405568500-
dc.contributor.authorscopusid56285163800-
dc.contributor.authorscopusid57189334144-
dc.contributor.authorscopusid14032568700-
dc.contributor.authorscopusid35466252100-
dc.contributor.authorscopusid35609452100-
dc.contributor.authorscopusid56006321500-
dc.contributor.authorscopusid7005499116-
dc.identifier.eissn1996-756X-
dc.description.firstpage35en_US
dc.relation.volume10951en_US
dc.investigacionCiencias de la Saluden_US
dc.investigacionIngeniería y Arquitecturaen_US
dc.type2Artículoen_US
dc.contributor.daisngid2096372-
dc.contributor.daisngid6051182-
dc.contributor.daisngid1812298-
dc.contributor.daisngid2864016-
dc.contributor.daisngid5142172-
dc.contributor.daisngid116294-
dc.contributor.daisngid506422-
dc.contributor.daisngid306847-
dc.description.notasSPIE Medical Imaging, 2019, San Diego, California, United Statesen_US
dc.description.numberofpages11en_US
dc.utils.revisionNoen_US
dc.contributor.wosstandardLinte, Cristian A.-
dc.contributor.wosstandardFei, Baowei-
dc.contributor.wosstandardWOS:Fabelo, H-
dc.contributor.wosstandardWOS:Halicek, M-
dc.contributor.wosstandardWOS:Ortega, S-
dc.contributor.wosstandardWOS:Szolna, A-
dc.contributor.wosstandardWOS:Morera, J-
dc.contributor.wosstandardWOS:Sarmiento, R-
dc.contributor.wosstandardWOS:Callico, GM-
dc.contributor.wosstandardWOS:Fei, BW-
dc.identifier.conferenceidevents121169-
dc.identifier.ulpgcen_US
dc.contributor.buulpgcBU-INGen_US
item.grantfulltextnone-
item.fulltextSin texto completo-
crisitem.author.deptGIR IUMA: Diseño de Sistemas Electrónicos Integrados para el procesamiento de datos-
crisitem.author.deptIU de Microelectrónica Aplicada-
crisitem.author.deptGIR IUMA: Diseño de Sistemas Electrónicos Integrados para el procesamiento de datos-
crisitem.author.deptIU de Microelectrónica Aplicada-
crisitem.author.deptGIR SIANI: Ingeniería biomédica aplicada a estimulación neural y sensorial-
crisitem.author.deptIU Sistemas Inteligentes y Aplicaciones Numéricas-
crisitem.author.deptDepartamento de Ciencias Médicas y Quirúrgicas-
crisitem.author.deptGIR IUMA: Diseño de Sistemas Electrónicos Integrados para el procesamiento de datos-
crisitem.author.deptIU de Microelectrónica Aplicada-
crisitem.author.deptDepartamento de Ingeniería Electrónica y Automática-
crisitem.author.deptGIR IUMA: Diseño de Sistemas Electrónicos Integrados para el procesamiento de datos-
crisitem.author.deptIU de Microelectrónica Aplicada-
crisitem.author.deptDepartamento de Ingeniería Electrónica y Automática-
crisitem.author.orcid0000-0002-9794-490X-
crisitem.author.orcid0000-0002-7519-954X-
crisitem.author.orcid0000-0002-4843-0507-
crisitem.author.orcid0000-0002-3784-5504-
crisitem.author.parentorgIU de Microelectrónica Aplicada-
crisitem.author.parentorgIU de Microelectrónica Aplicada-
crisitem.author.parentorgIU Sistemas Inteligentes y Aplicaciones Numéricas-
crisitem.author.parentorgIU de Microelectrónica Aplicada-
crisitem.author.parentorgIU de Microelectrónica Aplicada-
crisitem.author.fullNameFabelo Gómez, Himar Antonio-
crisitem.author.fullNameOrtega Sarmiento,Samuel-
crisitem.author.fullNameZbigniew Szolna,Adam-
crisitem.author.fullNameMorera Molina, Jesús Manuel-
crisitem.author.fullNameSarmiento Rodríguez, Roberto-
crisitem.author.fullNameMarrero Callicó, Gustavo Iván-
crisitem.event.eventsstartdate17-02-2019-
crisitem.event.eventsenddate19-02-2019-
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