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


  • Francisco José Fumero Batista Department of Computer Engineering and Systems, University of La Laguna https://orcid.org/0000-0001-9806-2477
  • Tinguaro Diaz-Aleman Servicio de Oftalmología, Hospital Universitario de Canarias
  • Jose Sigut Department of Computer Engineering and Systems, University of La Laguna
  • Silvia Alayon Department of Computer Engineering and Systems, University of La Laguna
  • Rafael Arnay Department of Computer Engineering and Systems, University of La Laguna
  • Denisse Angel-Pereira Servicio de Oftalmología, Hospital Universitario de Canarias




Convolutional Neural Networks, Deep Learning, Glaucoma Assessment, RIM-ONE


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.


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How to Cite

Fumero Batista, F. J., Diaz-Aleman, T., Sigut, J., Alayon, S., Arnay, R., & Angel-Pereira, D. (2020). RIM-ONE DL: A Unified Retinal Image Database for Assessing Glaucoma Using Deep Learning. Image Analysis & Stereology, 39(3), 161–167. https://doi.org/10.5566/ias.2346



Original Research Paper