Datasets › RITE

RITE (Retinal Images vessel Tree Extraction)

1 Jan 2013 archive 2025-07-28

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

Benchmarks archive 2025-07-28

All 2 leaderboards whose dataset resolves to this page shown (sort by any header). "First row" is the archive's own first row at snapshot, in the archive's row order; nothing here re-ranks and metric direction is not asserted.

Papers archive 2025-07-28

4 shown of 4 papers with a leaderboard row on this dataset's benchmarks, newest first. The archive's own "papers using this dataset" list was never published, so this is the benchmark-backed subset; the archive's count for this dataset is 5. The Syntology column is from Syntology's graph (read 2026-09-24), stated per sample; it is not part of any archive number.

DateSamples run Syntology
RRWNet: Recursive Refinement Network for effective retinal artery/vein segmentation and classification 1 1 5 Feb 2024 not harvested
KiU-Net: Overcomplete Convolutional Architectures for Biomedical Image and Volumetric Segmentation 1 1 4 Oct 2020 not harvested
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation 74 1 2 Nov 2015 ran 9 of 44 samples (35 unverified; 10 pointer-only for licence)
U-Net: Convolutional Networks for Biomedical Image Segmentation 487 1 18 May 2015 ran 510 of 757 samples (247 unverified; 426 pointer-only for licence)

Dataset loaders archive 2025-07-28

1 loader as listed in the archive; links are outbound and not re-checked here.

Tasks archive 2025-07-28

License archive 2025-07-28

No licence recorded in the archive. Absence here is not a statement about the dataset's terms.

Modalities archive 2025-07-28

Languages archive 2025-07-28

Variants archive 2025-07-28

  • RITE

1 variant name, as the archive lists them.

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