{"url":"/dataset/synth-colon","name":"Synth-Colon","full_name":null,"description_markdown":"Synthetic dataset for polyp segmentation. It\r\nis the first dataset generated using zero annotations from medical professionals.\r\nThe dataset is composed of 20 000 images with a resolution of 500×500. SynthColon additionally includes realistic colon images generated with a CycleGAN\r\nand the Kvasir training set images. Synth-Colon can also be used for the colon\r\ndepth estimation task  because it provides depth and 3D information for each\r\nimage. . In summary, Synth-Colon\r\nincludes:\r\n– Synthetic images of the colon and one polyp.\r\n– Masks indicating the location of the polyp.\r\n– Realistic images of the colon and polyps. Generated using CycleGAN and\r\nthe Kvasir dataset.\r\n– Depth images of the colon and polyp.\r\n– 3D meshes of the colon and polyp in OBJ format.","description_withheld":null,"homepage":"https://enric1994.github.io/synth-colon/","introduced_date":"2022-02-17","introduced_date_note":null,"introduced_by":{"paper":"/paper/synthetic-data-for-unsupervised-polyp","title":"Synthetic data for unsupervised polyp segmentation","first_author":"Enric Moreu","url":null},"license":null,"modalities":[],"tasks":[],"languages":[],"variants":["Synth-Colon"],"data_loaders":[],"num_papers_in_archive":3,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"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."}