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Automatic Identification and Intuitive Map Representation of the Epiretinal Membrane Presence in 3D OCT Volumes
dc.contributor.author | Baamonde, Sergio | |
dc.contributor.author | Moura, Joaquim de | |
dc.contributor.author | Novo Buján, Jorge | |
dc.contributor.author | Charlón, Pablo | |
dc.contributor.author | Ortega Hortas, Marcos | |
dc.date.accessioned | 2019-12-26T10:12:36Z | |
dc.date.available | 2019-12-26T10:12:36Z | |
dc.date.issued | 2019-11-29 | |
dc.identifier.citation | Baamonde, S.; de Moura, J.; Novo, J.; Charlón, P.; Ortega, M. Automatic Identification and Intuitive Map Representation of the Epiretinal Membrane Presence in 3D OCT Volumes. Sensors 2019, 19, 5269. https://doi.org/10.3390/s19235269 | es_ES |
dc.identifier.issn | 1424-8220 | |
dc.identifier.uri | http://hdl.handle.net/2183/24541 | |
dc.description.abstract | [Abstract] Optical Coherence Tomography (OCT) is a medical image modality providing high-resolution cross-sectional visualizations of the retinal tissues without any invasive procedure, commonly used in the analysis of retinal diseases such as diabetic retinopathy or retinal detachment. Early identification of the epiretinal membrane (ERM) facilitates ERM surgical removal operations. Moreover, presence of the ERM is linked to other retinal pathologies, such as macular edemas, being among the main causes of vision loss. In this work, we propose an automatic method for the characterization and visualization of the ERM’s presence using 3D OCT volumes. A set of 452 features is refined using the Spatial Uniform ReliefF (SURF) selection strategy to identify the most relevant ones. Afterwards, a set of representative classifiers is trained, selecting the most proficient model, generating a 2D reconstruction of the ERM’s presence. Finally, a post-processing stage using a set of morphological operators is performed to improve the quality of the generated maps. To verify the proposed methodology, we used 20 3D OCT volumes, both with and without the ERM’s presence, totalling 2428 OCT images manually labeled by a specialist. The most optimal classifier in the training stage achieved a mean accuracy of 91.9%. Regarding the post-processing stage, mean specificity values of 91.9% and 99.0% were obtained from volumes with and without the ERM’s presence, respectively. | es_ES |
dc.description.sponsorship | This work is supported by the Instituto de Salud Carlos III, Government of Spain and FEDER funds of the European Union through the DTS18/00136 research projects and by the Ministerio de Ciencia, Innovación y Universidades, Government of Spain through the DPI2015-69948-R and RTI2018-095894-B-I00 research projects. Moreover, this work has received financial support from the European Union (European Regional Development Fund—ERDF) and the Xunta de Galicia, Grupos de Referencia Competitiva, Ref. ED431C 2016-047. | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C 2016-047 | es_ES |
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/s19235269 | es_ES |
dc.rights | Atribución 4.0 Internacional (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 | Epiretinal membrane | es_ES |
dc.title | Automatic Identification and Intuitive Map Representation of the Epiretinal Membrane Presence in 3D OCT Volumes | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Sensors | es_ES |
UDC.volume | 19 | es_ES |
UDC.issue | 23 | es_ES |
UDC.startPage | 5269 | es_ES |
dc.identifier.doi | 10.3390/s19235269 |
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