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Automatic Tool for the Detection, Characterization and Intuitive Visualization of Macular Edema Regions in OCT Images
dc.contributor.author | Otero, Iago | |
dc.contributor.author | Vidal, Plácido | |
dc.contributor.author | Moura, Joaquim de | |
dc.contributor.author | Novo Buján, Jorge | |
dc.contributor.author | Ortega Hortas, Marcos | |
dc.date.accessioned | 2019-09-12T14:23:21Z | |
dc.date.available | 2019-09-12T14:23:21Z | |
dc.date.issued | 2019-08-01 | |
dc.identifier.citation | Otero, I.; Vidal, P.L.; Moura, J.d.; Novo, J.; Ortega, M. Automatic Tool for the Detection, Characterization and Intuitive Visualization of Macular Edema Regions in OCT Images. Proceedings 2019, 21, 36. https://doi.org/10.3390/proceedings2019021036 | es_ES |
dc.identifier.issn | 2504-3900 | |
dc.identifier.uri | http://hdl.handle.net/2183/23923 | |
dc.description.abstract | [Abstract] The methodology presented in this paper aims to detect pathological regions affected by one or more of the three clinically defined types of Diabetic Macular Edema (DME). Using representative samples extracted from Optical Coherence Tomography (OCT) images, three representative classifiers are trained to analyze new input images and create an intuitive visualization of the detection results. The trained models provided a satisfactory performance for all three defined types of DME, and the visual feedback can effectively assists clinical experts in the diagnosis of this representative and extended disease. | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G/01 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C 2016-047 | es_ES |
dc.description.sponsorship | Ministerio de Educación y Formación Profesional; 18CO1/006199. | es_ES |
dc.description.sponsorship | This research was funded by Instituto de Salud Carlos III grant number DTS18/00136, Ministerio de Ciencia, Innovación y Universidades grant numbers DPI 2015-69948-R and RTI2018-095894-B-I00, Xunta de Galicia through the accreditation of Centro Singular de Investigación 2016–2019, Ref. ED431G/01, Xunta de Galicia through Grupos de Referencia Competitiva, Ref. ED431C 2016-047 and Ministerio de Educación y Formación Profesional grant number 18CO1/006199. | |
dc.language.iso | eng | es_ES |
dc.publisher | MDPI AG | es_ES |
dc.relation | info:eu-repo/grantAgreement/MICINN/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/DTS18%2F00136/ES/Plataforma online para prevención y detección precoz de enfermedad vascular mediante análisis automatizado de información e imagen clínica | |
dc.relation | info: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 MAQUINA | |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-095894-B-I00/ES/DESARROLLO DE TECNOLOGIAS INTELIGENTES PARA DIAGNOSTICO DE LA DMAE BASADAS EN EL ANALISIS AUTOMATICO DE NUEVAS MODALIDADES HETEROGENEAS DE ADQUISICION DE IMAGEN OFTALMOLOGICA | |
dc.relation.uri | https://doi.org/10.3390/proceedings2019021036 | es_ES |
dc.rights | Atribución 4.0 Interancional (CC BY 4.0) | es_ES |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | * |
dc.subject | Computer-aided diagnosis | es_ES |
dc.subject | Retinal imaging | es_ES |
dc.subject | Optical coherence tomography | es_ES |
dc.subject | Macular edema | es_ES |
dc.title | Automatic Tool for the Detection, Characterization and Intuitive Visualization of Macular Edema Regions in OCT Images | es_ES |
dc.type | info:eu-repo/semantics/conferenceObject | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Proceedings | es_ES |
UDC.volume | 21 | es_ES |
UDC.issue | 1 | es_ES |
UDC.startPage | 36 | es_ES |
dc.identifier.doi | 10.3390/proceedings2019021036 | |
UDC.conferenceTitle | 2nd XoveTIC Conference, A Coruña, Spain, 5–6 September 2019. | es_ES |