Deep Retinal Image Understanding

  • Kevis-Kokitsi ManinisEmail author
  • Jordi Pont-Tuset
  • Pablo Arbeláez
  • Luc Van Gool
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9901)


This paper presents Deep Retinal Image Understanding (DRIU), a unified framework of retinal image analysis that provides both retinal vessel and optic disc segmentation. We make use of deep Convolutional Neural Networks (CNNs), which have proven revolutionary in other fields of computer vision such as object detection and image classification, and we bring their power to the study of eye fundus images. DRIU uses a base network architecture on which two set of specialized layers are trained to solve both the retinal vessel and optic disc segmentation. We present experimental validation, both qualitative and quantitative, in four public datasets for these tasks. In all of them, DRIU presents super-human performance, that is, it shows results more consistent with a gold standard than a second human annotator used as control.


Retinal vessel segmentation Optic disc segmentation Deep learning Convolutional neural networks Retinal image understanding 



Research funded by the EU Framework Programme for Research and Innovation - Horizon 2020 - Grant Agreement No. 645331 - EurEyeCase. We thank NVIDIA Corporation for donating the GPUs used in this project.


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Kevis-Kokitsi Maninis
    • 1
    Email author
  • Jordi Pont-Tuset
    • 1
  • Pablo Arbeláez
    • 2
  • Luc Van Gool
    • 1
    • 3
  1. 1.ETH ZürichZürichSwitzerland
  2. 2.Universidad de los AndesBogotáColombia
  3. 3.KU LeuvenLeuvenBelgium

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