{"url":"/dataset/rite","name":"RITE","full_name":"Retinal Images vessel Tree Extraction","description_markdown":"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).\r\n\r\nRITE 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. \r\n\r\n* The fundus photograph is inherited from DRIVE. \r\n* For the training set, the vessel reference standard is a modified version of 1st_manual from DRIVE. \r\n* For the test set, the vessel reference standard is 2nd_manual from DRIVE. \r\n* For the A/V reference standard, four types of vessels are labelled using four colors based on the vessel reference standard. \r\n* 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. \r\n* The fundus photograph is in tif format. And the vessel reference standard and the A/V reference standard are in png format.  \r\n\r\nThe dataset is described in more detail in our paper, which you will cite if you use the dataset in any way: \r\n\r\n 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","description_withheld":null,"homepage":"https://medicine.uiowa.edu/eye/rite-dataset","introduced_date":"2013-01-01","introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Medical","url":"/datasets/modality/medical"}],"tasks":[{"name":"Classification","url":"/task/classification-1","datasets_with_task":"/datasets/task/classification-1"},{"name":"Medical Image Segmentation","url":"/task/medical-image-segmentation","datasets_with_task":"/datasets/task/medical-image-segmentation"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["RITE"],"data_loaders":[{"repo":"https://github.com/zhouzhouhei/RITE-Dataset","url":"https://github.com/zhouzhouhei/RITE-Dataset","frameworks":["pytorch"]}],"num_papers_in_archive":5,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/medical-image-segmentation-on-rite","task":"Medical Image Segmentation","dataset_variant":"RITE","rows":3,"metrics":["Dice","Jaccard Index"],"first_row_in_archive_order":{"model":"KiU-Net","paper":"/paper/kiu-net-overcomplete-convolutional","metrics":{"Dice":"75.17","Jaccard Index":"60.37"},"code_links":[{"title":"jeya-maria-jose/KiU-Net-pytorch","url":"https://github.com/jeya-maria-jose/KiU-Net-pytorch"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/classification-on-rite","task":"Classification","dataset_variant":"RITE","rows":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"RRWNet","paper":"/paper/rrwnet-recursive-refinement-network-for","metrics":{"Accuracy":"0.9666"},"code_links":[{"title":"j-morano/rrwnet","url":"https://github.com/j-morano/rrwnet"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/rrwnet-recursive-refinement-network-for","title":"RRWNet: Recursive Refinement Network for effective retinal artery/vein segmentation and classification","date":"2024-02-05","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/kiu-net-overcomplete-convolutional","title":"KiU-Net: Overcomplete Convolutional Architectures for Biomedical Image and Volumetric Segmentation","date":"2020-10-04","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/segnet-a-deep-convolutional-encoder-decoder","title":"SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation","date":"2015-11-02","rows_on_this_dataset":1,"code_links":74,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":44,"samples_ran":9,"samples_unverified":35,"pointer_only_for_licence":10,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/u-net-convolutional-networks-for-biomedical","title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","date":"2015-05-18","rows_on_this_dataset":1,"code_links":487,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":757,"samples_ran":510,"samples_unverified":247,"pointer_only_for_licence":426,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":2,"samples_harvested":801,"samples_ran":519,"samples_unverified":282,"pointer_only_for_licence":436,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}