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Comparative and Behavioural Analysis of a Diffuse Paradigm for the Evaluation of Diabetic Macular Edema in OCT images
dc.contributor.author | Vidal, Plácido | |
dc.contributor.author | Moura, Joaquim de | |
dc.contributor.author | Díaz, Macarena | |
dc.contributor.author | Novo Buján, Jorge | |
dc.contributor.author | Ortega Hortas, Marcos | |
dc.date.accessioned | 2024-07-05T17:17:38Z | |
dc.date.available | 2024-07-05T17:17:38Z | |
dc.date.issued | 2021 | |
dc.identifier.citation | P. L. Vidal, J. de Moura, M. Díaz, J. Novo and M. Ortega, "Comparative and Behavioural Analysis of a Diffuse Paradigm for the Evaluation of Diabetic Macular Edema in OCT images," 2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS), Aveiro, Portugal, 2021, pp. 13-18, doi: 10.1109/CBMS52027.2021.00010. | es_ES |
dc.identifier.isbn | 978-1-6654-4121-6 | |
dc.identifier.issn | 2372-9198 | |
dc.identifier.uri | http://hdl.handle.net/2183/37772 | |
dc.description | The conference was held during June 7 to 9, 2021 Aveiro, Portugal. | es_ES |
dc.description | This version of the paper has been accepted for publication. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The final published paper is available online at: https://doi.org/10.1109/CBMS52027.2021.00010 | es_ES |
dc.description.abstract | [Abstract]: Nowadays, Diabetic Macular Edema (DME) is one of the leading causes of blindness in developed countries, and its characterized by the presence of pathological fluid accumulations inside the retinal layers. Currently, the main way to detect these fluid accumulations (as well as their severity) is through the use of Optical Coherence Tomography (OCT) imaging. In particular, this ophthalmological image modality allows a precise non-invasive analysis of the morphology of the retina and its structures. Due to the complexity of attempting to successfully segment these fluid accumulations, an alternative paradigm for their detection has been recently proposed. This paradigm, based on a diffuse representation of the pathological regions, creates an intuitive representation of the pathological regions based on a confidence map. Currently, there are only two approaches for this paradigm: one based on a predefined library of texture and intensity features with established machine learning algorithms and other based on deep learning methods. Both approaches have proven to offer satisfactory results, but each one of them performs better in different scenarios. In this work, we perform a complete analysis and comparison on the behaviour and performance of both strategies in a clinical screening scenario to evaluate the suitability of both approaches for the clinical practice as well as their performance as computer vision strategies. | es_ES |
dc.description.sponsorship | This research was funded by Instituto de Salud Carlos III, Government of Spain, DTS18/00136 research project; Ministerio de Ciencia e Innovación y Universidades, Government of Spain, RTI2018-095894-B-I00 research project, Ayudas para la formación de profesorado universitario (FPU), grant ref. FPU18/02271; Ministerio de Ciencia e Innovación, Government of Spain through the research project with reference PID2019-108435RB-I00 ; Consellería de Cultura, Educación e Universidade, Xunta de Galicia, Grupos de Referencia Competitiva, grant ref. ED431C 2020/24 ; Axencia Galega de Innovación (GAIN), Xunta de Galicia, grant ref. IN845D 2020/38 ; CITIC, Centro de Investigación de Galicia ref. ED431G 2019/01, receives financial support from Consellería de Educación, Universidade e Formación Profesional, Xunta de Galicia, through the ERDF (80%) and Secretaría Xeral de Universidades (20%). | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C 2020/24 | es_ES |
dc.description.sponsorship | Xunta de Galicia; IN845D 2020/38 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G 2019/01 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Institute of Electrical and Electronics Engineers | es_ES |
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 | es_ES |
dc.relation | info:eu-repo/grantAgreement/MECD/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/FPU18%2F02271/ES/ | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-108435RB-I00/ES/CUANTIFICACION Y CARACTERIZACION COMPUTACIONAL DE IMAGEN MULTIMODAL OFTALMOLOGICA: ESTUDIOS EN ESCLEROSIS MULTIPLE | es_ES |
dc.relation.uri | https://doi.org/10.1109/CBMS52027.2021.00010 | es_ES |
dc.rights | © 2021 IEEE | es_ES |
dc.subject | Optical Coherence Tomography | es_ES |
dc.subject | Retinal Imaging | es_ES |
dc.subject | Computer aided detection and diagnosis | es_ES |
dc.subject | Deep Learning | es_ES |
dc.subject | Feature selection | es_ES |
dc.subject | Feature extraction | es_ES |
dc.subject | Confidence map | es_ES |
dc.title | Comparative and Behavioural Analysis of a Diffuse Paradigm for the Evaluation of Diabetic Macular Edema 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.volume | 2021 | es_ES |
UDC.startPage | 13 | es_ES |
UDC.endPage | 18 | es_ES |
dc.identifier.doi | 10.1109/CBMS52027.2021.00010 | |
UDC.conferenceTitle | IEEE International Symposium on Computer-Based Medical Systems (CBMS) | es_ES |