RITE

Retinal Images vessel Tree Extraction

Dataset Information
Modalities
Images, Medical
Languages
English
Introduced
2013
License
Unknown
Homepage

Overview

The RITE (Retinal Images vessel Tree Extraction) is a database that enables comparative studies on segmentation or classification of arteries and veins on retinal fundus images, which is established based on the public available DRIVE database (Digital Retinal Images for Vessel Extraction).

RITE contains 40 sets of images, equally separated into a training subset and a test subset, the same as DRIVE. The two subsets are built from the corresponding two subsets in DRIVE. For each set, there is a fundus photograph, a vessel reference standard, and a Arteries/Veins (A/V) reference standard.

  • The fundus photograph is inherited from DRIVE.
  • For the training set, the vessel reference standard is a modified version of 1st_manual from DRIVE.
  • For the test set, the vessel reference standard is 2nd_manual from DRIVE.
  • For the A/V reference standard, four types of vessels are labelled using four colors based on the vessel reference standard.
  • Arteries are labelled in red; veins are labelled in blue; the overlapping of arteries and veins are labelled in green; the vessels which are uncertain are labelled in white.
  • The fundus photograph is in tif format. And the vessel reference standard and the A/V reference standard are in png format.

The dataset is described in more detail in our paper, which you will cite if you use the dataset in any way:

Hu Q, Abràmoff MD, Garvin MK. Automated separation of binary overlapping trees in low-contrast color retinal images. Med Image Comput Comput Assist Interv. 2013;16(Pt 2):436-43. PubMed PMID: 24579170 https://doi.org/10.1007/978-3-642-40763-5_54

Variants: RITE

Associated Benchmarks

This dataset is used in 2 benchmarks:

Recent Benchmark Submissions

Task Model Paper Date
Classification RRWNet RRWNet: Recursive Refinement Network for … 2024-02-05
Medical Image Segmentation KiU-Net KiU-Net: Overcomplete Convolutional Architectures for … 2020-10-04
Medical Image Segmentation SegNet SegNet: A Deep Convolutional Encoder-Decoder … 2015-11-02
Medical Image Segmentation U-Net U-Net: Convolutional Networks for Biomedical … 2015-05-18

Research Papers

Recent papers with results on this dataset: