Mostrar o rexistro simple do ítem

dc.contributor.authorSamagaio, Gabriela
dc.contributor.authorMoura, Joaquim de
dc.contributor.authorNovo Buján, Jorge
dc.contributor.authorOrtega Hortas, Marcos
dc.date.accessioned2024-06-07T14:39:49Z
dc.date.available2024-06-07T14:39:49Z
dc.date.issued2018
dc.identifier.citationSamagaio, G., de Moura, J., Novo, J., & Ortega, M. (2018). "Automatic segmentation of diffuse retinal thickening edemas using Optical Coherence Tomography images" in International Conference on Knowledge Based and Intelligent Information and Engineering Systems, KES2018. Procedia Computer Science, 126, 472-481. https://doi.org/10.1016/j.procs.2018.07.281es_ES
dc.identifier.issn1877-0509
dc.identifier.urihttp://hdl.handle.net/2183/36842
dc.description.abstract[Abstract]: Diabetic retinopathy is one of the leading causes of vision impairment that is commonly associated to the Macular Edema (ME) disease. The Diffuse Retinal Thickening (DRT) is a ME type derived from the local intraretinal fluid accumulation in the lower retinal layers, producing significant morphological alterations in the eye fundus. The presence and properties of these intraretinal fluids are used by the ophthalmologists as significant indicators of the clinical stage of the ME disease. Given that, the precise identification and segmentation of the DRT edema type allow the early diagnosis of the ME disease which, therefore, permits a better adjustment of the treatments, reducing their costs as well as improving the life quality of the patients. This paper proposes a novel methodology for the automatic identification and segmentation of the DRT edemas using Optical Coherence Tomography (OCT) images as source of information. Firstly, the method identifies four of the principal retinal layers that are used as reference to delimit the outer retina, region where the DRT edemas are typically originated. Inside this region, a large and heterogeneous set of features was defined to recognize the characteristic “sponge-like” patterns of the DRT edema, using intensity, texture and clinically-defined features. For this analysis, four representative classifiers were employed with the best subsets of previously selected features. This methodology was tested using 70 OCT images from where 560 samples were extracted with the presence and absence of DRT edemas. The best results were achieved by the 7-kNN classifier, reaching in the detection stage an accuracy of 0.9366, whereas in the segmentation stage obtained values of 0.6625 and 0.7899 for the Jaccard and Dice coefficients, respectively.es_ES
dc.description.sponsorshipThis work is supported by the Instituto de Salud Carlos III, Government of Spain and FEDER funds of the European Union through the PI14/02161 and the DTS15/00153 research projects and by the Ministerio de Economía y Competitividad, Government of Spain through the DPI2015-69948-R research project. Also, this work has received financial support from the European Union (European Regional Development Fund - ERDF) and the Xunta de Galicia, Centro singular de investigación de Galicia accreditation 2016-2019, Ref. ED431G/01; and Grupos de Referencia Competitiva, Ref. ED431C 2016-047.es_ES
dc.description.sponsorshipXunta de Galicia; ED431G/01es_ES
dc.description.sponsorshipXunta de Galicia; ED431C 2016-047es_ES
dc.language.isoenges_ES
dc.publisherElsevier B.V.es_ES
dc.relationinfo:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/DPI2015-69948-R/ES/IDENTIFICACION Y CARACTERIZACION DEL EDEMA MACULAR DIABETICO MEDIANTE ANALISIS AUTOMATICO DE TOMOGRAFIAS DE COHERENCIA OPTICA Y TECNICAS DE APRENDIZAJE MAQUINAes_ES
dc.relationinfo:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/PI14%2F02161/ES/DESARROLLO DE UN SISTEMA AUTOMÁTICO PARA EL CÁLCULO Y VISUALIZACIÓN DE PROPIEDADES ANATÓMICAS DE LA RETINA EN SD-OCT Y SU CORRELACIÓN CON ANÁLISIS FUNCIONALES HETEROGÉNEOS DE LA VISIÓNes_ES
dc.relationinfo:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/DTS15%2F00153/ES/SIRIUS - SISTEMA DE ANÁLISIS DE MICROCIRCULACIÓN RETINIANA: EVALUACIÓN MULTIDISCIPLINAR E INTEGRACIÓN EN PROTOCOLOS CLÍNICOSes_ES
dc.relation.urihttps://doi.org/10.1016/j.procs.2018.07.281es_ES
dc.rightsAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)es_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subjectComputer-aided diagnosises_ES
dc.subjectOptical Coherence Tomographyes_ES
dc.subjectDiffuse retinal thickening regiones_ES
dc.subjectSegmentationes_ES
dc.titleAutomatic Segmentation of Diffuse Retinal Thickening Edemas Using Optical Coherence Tomography Imageses_ES
dc.typeinfo:eu-repo/semantics/conferenceObjectes_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES
UDC.journalTitleProcedia Computer Sciencees_ES
UDC.volume126es_ES
UDC.startPage472es_ES
UDC.endPage481es_ES
dc.identifier.doi10.1016/j.procs.2018.07.281
UDC.conferenceTitleInternational Conference on Knowledge Based and Intelligent Information and Engineering Systems, KES 2018es_ES


Ficheiros no ítem

Thumbnail
Thumbnail

Este ítem aparece na(s) seguinte(s) colección(s)

Mostrar o rexistro simple do ítem