RT info:eu-repo/semantics/article
T1 RIM-ONE DL: A unified retinal image database for assessing Glaucoma using Deep Learning
A1 Alayón Miranda, Silvia
A1 Fumero Batista, Francisco Jose
A1 Díaz-Alemán, Tinguaro
A1 Sigut Saavedra, José Francisco
A1 Arnay del Arco, Rafael
A1 Angel-Pereira, Denisse
K1 Convolutional Neural Networks, Deep Learning, Glaucoma Assessment, RIM-ONE
AB The 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.
SN 1854-5165
YR 2020
FD 2020
LK http://riull.ull.es/xmlui/handle/915/35144
UL http://riull.ull.es/xmlui/handle/915/35144
LA en
DS Repositorio institucional de la Universidad de La Laguna
RD 12-may-2024