# Deep Retinal Image Understanding

Source: [Deep Retinal Image Understanding](<https://www.sota2.com/research/paper/68fa62ee1e209131ce9fbf43>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T20:29:18.716Z

Authors: Kevis-Kokitsi Maninis, Jordi Pont-Tuset, Pablo Arbel\\'aez, Luc Van Gool

Published: 2016-09-05T11:20:30.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1609.01103>)

[PDF](<https://arxiv.org/pdf/1609.01103.pdf>)