Mostrar o rexistro simple do ítem
Fully Automatic Epiretinal Membrane Segmentation in OCT Scans Using Convolutional Networks
dc.contributor.author | Gende, M. | |
dc.contributor.author | Moura, Joaquim de | |
dc.contributor.author | Novo Buján, Jorge | |
dc.contributor.author | Ortega Hortas, Marcos | |
dc.date.accessioned | 2024-05-09T08:52:45Z | |
dc.date.available | 2024-05-09T08:52:45Z | |
dc.date.issued | 2022-06 | |
dc.identifier.citation | Gende, M., de Moura, J., Novo, J., & Ortega, M. (2022). Fully Automatic Epiretinal Membrane Segmentation in OCT Scans Using Convolutional Networks. In R. El Ouazzani, M. Fattah, & N. Benamar (Eds.), AI Applications for Disease Diagnosis and Treatment (pp. 88-121). IGI Global. https://doi.org/10.4018/978-1-6684-2304-2.ch004 | es_ES |
dc.identifier.isbn | 9781668423042 | |
dc.identifier.uri | http://hdl.handle.net/2183/36437 | |
dc.description.abstract | [Absctract]: The epiretinal membrane (ERM) is an ocular pathology that can cause visual distortions. To prevent a loss of vision, symptomatic ERM needs to be removed before it can cause irreversible damage. In order to do this, the ERM needs to be located early, so that it can be peeled from the retina. This chapter explores an automatic methodology for ERM segmentation, as well as its intuitive visualization in the form of colour maps. To do this, visual features that are compatible with ERM presence are extracted from ophthalmologic images by using computer vision algorithms and deep learning models. This methodology achieved satisfactory results, reaching a dice coefficient of 0.826 and a Jaccard index of 0.714, contributing to highlight the applicability of deep learning models for the detection of pathological signs in medical images. | es_ES |
dc.description.sponsorship | This research was funded by Instituto de Salud Carlos III, Government of Spain, [research project DTS18/00136]; Ministerio de Ciencia e Innovación y Universidades, Government of Spain, [research project RTI2018-095894-B-I00]; Ministerio de Ciencia e Innovación, Government of Spain [research project PID2019-108435RB-I00]; Consellería de Cultura, Educación e Universidade, Xunta de Galicia, Grupos de Referencia Competitiva, [grant ref. ED431C 2020/24], predoctoral [grant ref. ED481A 2021/161] and postdoctoral [grant ref. ED481B 113 Fully Automatic Epiretinal Membrane Segmentation 2021/059]; Axencia Galega de Innovación (GAIN), Xunta de Galicia, [grant ref. IN845D 2020/38]; CITIC, Centro de Investigación de Galicia [grant 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; ED481A 2021/161 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED481B 2021/059 | 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 | IGI Global | 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/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 | 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/CUANTIFICACIÓN Y CARACTERIZACIÓN COMPUTACIONAL DE IMAGEN MULTIMODAL OFTALMOLÓGICA: ESTUDIOS EN ESCLEROSIS MÚLTIPLE | es_ES |
dc.relation.uri | https://doi.org/10.4018/978-1-6684-2304-2.ch004 | es_ES |
dc.rights | Copyright: © 2022 IGI Global | es_ES |
dc.subject | Artificial Neural Network | es_ES |
dc.subject | Epiretinal Membrane (ERM) | es_ES |
dc.subject | Fovea | es_ES |
dc.subject | Inner Limiting Membrane (ILM) | es_ES |
dc.subject | Macula | es_ES |
dc.subject | Optical Coherence Tomography (OCT) | es_ES |
dc.subject | Retina | es_ES |
dc.subject | Segmentation | es_ES |
dc.title | Fully Automatic Epiretinal Membrane Segmentation in OCT Scans Using Convolutional Networks | es_ES |
dc.type | info:eu-repo/semantics/bookPart | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | AI Applications for Disease Diagnosis and Treatment | es_ES |
UDC.startPage | 88 | es_ES |
UDC.endPage | 121 | es_ES |