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dc.contributor.authorSigut Saavedra, José Francisco 
dc.contributor.authorFumero Batista, Francisco José 
dc.contributor.authorDíaz Alemán, Tinguaro
dc.contributor.authorAlayón, Silvia
dc.contributor.authorArnay, Rafael
dc.contributor.authorAngel-Pereira, Denisse
dc.date.accessioned2023-12-27T21:06:44Z
dc.date.available2023-12-27T21:06:44Z
dc.date.issued2020
dc.identifier.issn1854-5165
dc.identifier.urihttp://riull.ull.es/xmlui/handle/915/35104
dc.description.abstractThe first version of the Retinal IMage database for Optic Nerve Evaluation (RIM-ONE) was published in 2011. This was followed by two more, turning it into one of the most cited public retinography databases for evaluating glaucoma. Although it was initially intended to be a database with reference images for segmenting the optic disc, in recent years we have observed that its use has been more oriented toward training and testing deep learning models. The recent REFUGE challenge laid out some criteria that a set of images of these characteristics must satisfy to be used as a standard reference for validating deep learning methods that rely on the use of these data. This, combined with the certain confusion and even improper use observed in some cases of the three versions published, led us to consider revising and combining them into a new, publicly available version called RIM-ONE DL (RIM-ONE for Deep Learning). This paper describes this set of images, consisting of 313 retinographies from normal subjects and 172 retinographies from patients with glaucoma. All of these images have been assessed by two experts and include a manual segmentation of the disc and cup. It also describes an evaluation benchmark with different models of well-known convolutional neural networks.en
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.relation.ispartofseriesImage Analysis & Stereology 2020 v.39 n.3, 161-167
dc.rightsLicencia Creative Commons (Reconocimiento-No comercial-Sin obras derivadas 4.0 Internacional)
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.es_ES
dc.titleRIM-ONE DL: A Unified Retinal Image Database for Assessing Glaucoma Using Deep Learning
dc.typeinfo:eu-repo/semantics/article
dc.identifier.doi10.5566/IAS.2346
dc.subject.keywordConvolutional Neural Networks
dc.subject.keywordDeep Learning
dc.subject.keywordGlaucoma Assessment
dc.subject.keywordRIM-ONE


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Licencia Creative Commons (Reconocimiento-No comercial-Sin obras derivadas 4.0 Internacional)
Except where otherwise noted, this item's license is described as Licencia Creative Commons (Reconocimiento-No comercial-Sin obras derivadas 4.0 Internacional)