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dc.rights.licenseAttribution 4.0 International*
dc.contributor.authorRomero-Bascones, David
dc.contributor.authorBarrenechea, Maitane
dc.contributor.authorAyala, Unai
dc.contributor.otherMurueta Goyena, Ane
dc.contributor.otherGaldós, Marta
dc.contributor.otherGómez Esteban, Juan Carlos
dc.contributor.otherGabilondo Cuellar, Iñigo
dc.date.accessioned2021-06-04T13:03:14Z
dc.date.available2021-06-04T13:03:14Z
dc.date.issued2021
dc.identifier.issn1099-4300en
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=163729en
dc.identifier.urihttps://hdl.handle.net/20.500.11984/5310
dc.description.abstractDisentangling the cellular anatomy that gives rise to human visual perception is one of the main challenges of ophthalmology. Of particular interest is the foveal pit, a concave depression located at the center of the retina that captures light from the gaze center. In recent years, there has been a growing interest in studying the morphology of the foveal pit by extracting geometrical features from optical coherence tomography (OCT) images. Despite this, research has devoted little attention to comparing existing approaches for two key methodological steps: the location of the foveal center and the mathematical modelling of the foveal pit. Building upon a dataset of 185 healthy subjects imaged twice, in the present paper the image alignment accuracy of four different foveal center location methods is studied in the first place. Secondly, state-of-the-art foveal pit mathematical models are compared in terms of fitting error, repeatability, and bias. The results indicate the importance of using a robust foveal center location method to align images. Moreover, we show that foveal pit models can improve the agreement between different acquisition protocols. Nevertheless, they can also introduce important biases in the parameter estimates that should be considered.es
dc.description.sponsorshipGobierno Vascoes
dc.language.isoengen
dc.publisherMDPIen
dc.rights© 2021 by the authors. Licensee MDPIen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectoptical coherence tomographyen
dc.subjectretinaen
dc.subjectfoveaen
dc.subjectretinal imagingen
dc.titleFoveal Pit Morphology Characterization: A Quantitative Analysis of the Key Methodological Stepsen
dcterms.accessRightshttp://purl.org/coar/access_right/c_abf2en
dcterms.sourceEntropyen
local.contributor.groupTeoría de la señal y comunicacioneses
local.description.peerreviewedtrueen
local.identifier.doihttps://doi.org/10.3390/e23060699en
local.relation.projectIDGV/Ayudas a proyectos de investigación y desarrollo en salud 2020/2020333033/CAPV/Aplicación clínica de la inteligencia artificial sobre imágenes de OCT de retina para la clasificación precoz y monitorización de pacientes con enfermedad de Parkinson/OCTen
local.rights.publicationfeeAPCen
local.rights.publicationfeeamount810 EURen
local.contributor.otherinstitutionhttps://ror.org/000xsnr85es
local.contributor.otherinstitutionhttps://ror.org/0061s4v88es
local.contributor.otherinstitutionhttps://ror.org/03nzegx43es
local.contributor.otherinstitutionhttps://ror.org/01cc3fy72es
local.source.detailsVol. 23. N. 6. N. artículo 699, 2021en
oaire.format.mimetypeapplication/pdf
oaire.file$DSPACE\assetstore
oaire.resourceTypehttp://purl.org/coar/resource_type/c_6501en
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85en


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Attribution 4.0 International
Except where otherwise noted, this item's license is described as Attribution 4.0 International