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dc.contributor.authorLópez Varela, Emilio*
dc.contributor.authorde Moura Ramos, Jose Joaquim*
dc.contributor.authorNovo Buján, Jorge*
dc.contributor.authorFernández-Vigo, J.I.*
dc.contributor.authorMoreno-Morillo, F.J.*
dc.contributor.authorOrtega Hortas, Marcos*
dc.date.accessioned2025-09-09T11:23:43Z
dc.date.available2025-09-09T11:23:43Z
dc.date.issued2023
dc.identifier.citationLópez-Varela E, de Moura J, Novo J, Fernández-Vigo JI, Moreno-Morillo FJ, Ortega M. Fully automatic segmentation and monitoring of choriocapillaris flow voids in OCTA images. Computerized Medical Imaging and Graphics. 2023;104.
dc.identifier.issn1879-0771
dc.identifier.otherhttps://portalcientifico.sergas.gal//documentos/63d5b3dcf851ee1ba3e9ebff
dc.identifier.urihttp://hdl.handle.net/20.500.11940/21521
dc.description.abstractOptical coherence tomography angiography (OCTA) is a non-invasive ophthalmic imaging modality that is widely used in clinical practice. Recent technological advances in OCTA allow imaging of blood flow deeper than the retinal layers, at the level of the choriocapillaris (CC), where a granular image is obtained showing a pattern of bright areas, representing blood flow, and a pattern of small dark regions, called flow voids (FVs). Several clinical studies have reported a close correlation between abnormal FVs distribution and multiple diseases, so quantifying changes in FVs distribution in CC has become an area of interest for many clinicians. However, CC OCTA images present very complex features that make it difficult to correctly compare FVs during the monitoring of a patient. In this work, we propose fully automatic approaches for the segmentation and monitoring of FVs in CC OCTA images. First, a baseline approach, in which a fully automatic segmentation methodology based on local contrast enhancement and global thresholding is proposed to segment FVs and measure changes in their distribution in a straightforward manner. Second, a robust approach in which, prior to the use of our segmentation methodology, an unsupervised trained neural network is used to perform a deformable registration that aligns inconsistencies between images acquired at different time instants. The proposed approaches were tested with CC OCTA images collected during a clinical study on the response to photodynamic therapy in patients affected by chronic central serous chorioretinopathy (CSC), demonstrating their clinical utility. The results showed that both approaches are accurate and robust, surpassing the state of the art, therefore improving the efficacy of FVs as a biomarker to monitor the patient treatments. This gives great potential for the clinical use of our methods, with the possibility of extending their use to other pathologies or treatments associated with this type of imaging.
dc.description.sponsorshipThis research was funded by Instituto de Salud Carlos III, Government of Spain, DTS18/00136 research project; Ministerio de Ciencia e Innovacion y Universidades, Government of Spain, RTI2018-095894-B-I00research project; Ministerio de Ciencia e Innovacion, Government of Spain through the research projects with references PID2019-108435RB-I00; TED2021-131201B-I00 and PDC2022-133132-I00; Conselleria de Cultura, Educacion e Universidade, Xunta de Galicia through the postdoctoral, grant ref. ED481B-2021-059; and Grupos de Referencia Competitiva, grant ref. ED431C 2020/24; Axencia Galega de Innovacion (GAIN), Xunta de Galicia, grant ref. IN845D 2020/38; CITIC, as Research Center accredited by Galician University System, is funded by Conselleria de Cultura, Educacion e Universidade from Xunta de Galicia, supported in an 80 % through ERDF Funds, ERDF Operational Programme Galicia 2014-2020, and the remaining 20 % by Secretaria Xeral de Universidades, grant ref. ED431G 2019/01. Emilio Lopez Varela acknowledges its support under FPI Grant Program through PID2019-108435RB-I00 project. Funding for open access charge: Universidade da Coruna/CISUG.
dc.languageeng
dc.rightsAttribution 4.0 International (CC BY 4.0)*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subject.meshHumans *
dc.subject.meshFluorescein Angiography *
dc.subject.meshTomography, Optical Coherence *
dc.subject.meshRetina *
dc.subject.meshPhotochemotherapy *
dc.subject.meshChoroid *
dc.titleFully automatic segmentation and monitoring of choriocapillaris flow voids in OCTA images
dc.typeArtigo
dc.authorsophosLópez-Varela, E.; de Moura, J.; Novo, J.; Fernández-Vigo, J.I.; Moreno-Morillo, F.J.; Ortega, M.
dc.identifier.doi10.1016/j.compmedimag.2022.102172
dc.identifier.sophos63d5b3dcf851ee1ba3e9ebff
dc.journal.titleComputerized Medical Imaging and Graphics*
dc.organizationInstituto de Investigación Biomédica de A Coruña (INIBIC)
dc.organizationServizo Galego de Saúde::Áreas Sanitarias (A.S.) - Instituto de Investigación Biomédica de A Coruña (INIBIC)
dc.organizationInstituto de Investigación Biomédica de A Coruña (INIBIC)
dc.organizationInstituto de Investigación Biomédica de A Coruña (INIBIC)
dc.relation.projectIDInstituto de Salud Carlos III, Govern-ment of Spain [DTS18/00136]
dc.relation.projectIDInstituto de Salud Carlos III, Government of Spain
dc.relation.projectIDMinisterio de Ciencia e Innovacion y Universidades, Government of Spain [DTS18/00136]
dc.relation.projectIDMinisterio de Ciencia e Innovacion, Government of Spain [RTI2018-095894-B-I00]
dc.relation.projectIDConselleria de Cultura, Educacion e Universidade, Xunta de Galicia [PID2019-108435RB-I00, TED2021-131201B-I00, PDC2022-133132-I00]
dc.relation.projectIDGrupos de Referencia Competitiva [ED481B-2021-059]
dc.relation.projectIDAxencia Galega de Innovacion (GAIN), Xunta de Galicia [ED431C 2020/24]
dc.relation.projectIDConselleria de Cultura, Educacion e Universidade from Xunta de Galicia through ERDF Funds, ERDF Operational Programme Galicia 2014-2020 [IN845D 2020/38]
dc.relation.projectIDSecretaria Xeral de Universidades
dc.relation.projectIDFPI Grant Program [ED431G 2019/01]
dc.relation.projectIDUniversidade da Coruna/CISUG [108435RB-I00]
dc.relation.publisherversionhttps://doi.org/10.1016/j.compmedimag.2022.102172
dc.rights.accessRightsopenAccess*
dc.subject.keywordINIBIC
dc.subject.keywordAS A Coruña
dc.subject.keywordINIBIC
dc.subject.keywordINIBIC
dc.subject.keywordINIBIC
dc.typefidesArtículo Científico (incluye Original, Original breve, Revisión Sistemática y Meta-análisis)
dc.typesophosArtículo Original
dc.volume.number104


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Attribution 4.0 International (CC BY 4.0)
Excepto si se señala otra cosa, la licencia del ítem se describe como Attribution 4.0 International (CC BY 4.0)