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Choroid segmentation in non-EDI OCT images of multiple sclerosis patients
dc.contributor.author | López-Varela, Emilio | |
dc.contributor.author | Barreira, Noelia | |
dc.contributor.author | Olivier Pascual, Nuria | |
dc.contributor.author | García Ben, Emma | |
dc.contributor.author | Rubio Cid, Sara | |
dc.contributor.author | Penedo, Manuel | |
dc.date.accessioned | 2024-05-21T16:19:02Z | |
dc.date.available | 2024-05-21T16:19:02Z | |
dc.date.issued | 2023 | |
dc.identifier.citation | López-Varela, E., Barreira, N., Pascual, N., Garcia Ben, E., Rubio Cid, S., & Penedo, M. G. (2023). Choroid segmentation in non-EDI OCT images of multiple sclerosis patients. In Proceedings of V XoveTIC Conference. XoveTIC, A. Leitao and L. Ramos (eds.) . Kalpa Publications in Computing, Vol. 14, pp. 10-13. . https://doi.org/10.29007/8q52 | es_ES |
dc.identifier.issn | 2515-1762 | |
dc.identifier.uri | http://hdl.handle.net/2183/36564 | |
dc.description.abstract | [Abstract]: Optical coherence tomography (OCT) is a non-invasive diagnostic technique that can image ocular structures. Recently, this imaging technique has been used to diagnose and monitor patients with multiple sclerosis (MS), as several clinical studies have linked the development of MS to various changes in the eye. Among the different structures, one of the relevant biomarkers for MS analysis is the choroid. Systems such as Enhanced Depth Imaging (EDI) provide detailed images of the choroid region. However, OCT images are not routinely captured using this technology unless the study is specifically focused on choroidal analysis. In this work we propose a robust approach, based on convolutional neural networks to segment the choroid in non-EDI OCT images. The results obtained show that the proposed network manages to delimit the inferior contour of the choroid in a similar way to the experts. | 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.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; Ministerio de Ciencia e Innovaciónn, 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%). Emilio López Varela acknowledges its support under FPI Grant Program through PID2019-108435RB-I00 project. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | A. Leitao and L. Ramos (eds.) | 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/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/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/10.29007/8q52 | es_ES |
dc.rights | © 2023, the Authors. | es_ES |
dc.subject | Choroid segmentation | es_ES |
dc.subject | Convolutional Neural Network | es_ES |
dc.subject | Deep learning | es_ES |
dc.subject | Multiple Sclerosis | es_ES |
dc.subject | Optical Coherence Tomography | es_ES |
dc.title | Choroid segmentation in non-EDI OCT images of multiple sclerosis patients | es_ES |
dc.type | info:eu-repo/semantics/conferenceObject | es_ES |
dc.type | info:eu-repo/semantics/conferenceObject | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.volume | 14 | es_ES |
UDC.startPage | 10 | es_ES |
UDC.endPage | 13 | es_ES |
UDC.conferenceTitle | V XoveTIC Conference. XoveTIC 2022 | es_ES |