RIM-ONE DL: A Unified Retinal Image Database for Assessing Glaucoma Using Deep Learning
Fecha
2020Resumen
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.