{"url":"/dataset/hutu-80","name":"HuTu 80","full_name":"HuTu 80 cell populations","description_markdown":"The image set contains 180 high-resolution color microscopic images of human duodenum adenocarcinoma HuTu 80 cell populations obtained in an in vitro scratch assay (for the details of the experimental protocol, we refer to (Liang et al., 2007)). Briefly, cells were seeded in 12-well culture plates ($20 \\times 10^3$ cells per well) and grown to form a monolayer with 85\\% or more confluency. Then the cell monolayer was scraped in a straight line using a pipette tip ($200 \\mu L$). The debris was removed by washing with a growth medium and the medium in wells was replaced. The scratch areas were marked to obtain the same field during the image acquisition. Images of the scratches were captured immediately following the scratch formation, as well as after 24, 48 and 72 h of cultivation.\r\n\r\nImages were obtained with the Zeiss Axio Observer 1.0 microscope (Carl Zeiss AG, Oberkochen, Germany) with 400x magnification. All images have been manually annotated by domain experts as a part of the original experimental [study](https://link.springer.com/article/10.1134/S0026893320010173). Here we use these manual annotations as a reference (``ground truth'').  To improve the reproducibility of our analysis, we made corresponding images and their manual annotations fully available at [https://gitlab.com/digiratory/biomedimaging/bcanalyzer](https://bit.ly/3hlvli9).","description_withheld":null,"homepage":"https://bit.ly/3hlvli9","introduced_date":"2022-09-22","introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Biomedical","url":"/datasets/modality/biomedical"}],"tasks":[{"name":"Semantic Segmentation","url":"/task/semantic-segmentation","datasets_with_task":"/datasets/task/semantic-segmentation"},{"name":"Image Segmentation","url":"/task/image-segmentation","datasets_with_task":"/datasets/task/image-segmentation"}],"languages":[],"variants":["HuTu 80"],"data_loaders":[],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/image-segmentation-on-hutu-80","task":"Image Segmentation","dataset_variant":"HuTu 80","rows":2,"metrics":["Dice"],"first_row_in_archive_order":{"model":"UNetR","paper":"/paper/segmentation-of-patchy-areas-in-biomedical","metrics":{"Dice":"0.9843"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/segmentation-of-patchy-areas-in-biomedical","title":"Segmentation of patchy areas in biomedical images based on local edge density estimation","date":"2022-09-22","rows_on_this_dataset":2,"code_links":0,"syntology":null}],"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."}