{"url":"/dataset/evican","name":"EVICAN","full_name":null,"description_markdown":"Deep learning use for quantitative image analysis is exponentially increasing. However, training accurate, widely deployable deep learning algorithms requires a plethora of annotated (ground truth) data. Image collections must contain not only thousands of images to provide sufficient example objects (i.e. cells), but also contain an adequate degree of image heterogeneity. We present a new dataset, EVICAN-Expert visual cell annotation, comprising partially annotated grayscale images of 30 different cell lines from multiple microscopes, contrast mechanisms and magnifications that is readily usable as training data for computer vision applications. With 4600 images and ∼26 000 segmented cells, our collection offers an unparalleled heterogeneous training dataset for cell biology deep learning application development. The dataset is freely available (https://edmond.mpdl.mpg.de/imeji/collection/l45s16atmi6Aa4sI?q=).","description_withheld":null,"homepage":"https://edmond.mpdl.mpg.de/dataset.xhtml;jsessionid=294bca02ef53cdc35118d0470ef7?persistentId=doi%3A10.17617%2F3.AJBV1S&version=&q=&fileTypeGroupFacet=&fileAccess=&fileSortField=type","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[],"tasks":[{"name":"Cell Segmentation","url":"/task/cell-segmentation","datasets_with_task":"/datasets/task/cell-segmentation"}],"languages":[],"variants":["EVICAN"],"data_loaders":[],"num_papers_in_archive":2,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/cell-segmentation-on-evican","task":"Cell Segmentation","dataset_variant":"EVICAN","rows":2,"metrics":["mask AP","mask AP50"],"first_row_in_archive_order":{"model":"DeepCeNS","paper":"/paper/deepcens-an-end-to-end-pipeline-for-cell-and","metrics":{"mask AP":"52.56","mask AP50":"83.40"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/deepcens-an-end-to-end-pipeline-for-cell-and","title":"DeepCeNS: An end-to-end Pipeline for Cell and Nucleus Segmentation in Microscopic Images","date":"2021-07-18","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/evican-a-balanced-dataset-for-algorithm","title":"EVICAN-a balanced dataset for algorithm development in cell and nucleus segmentation","date":"2020-03-27","rows_on_this_dataset":1,"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."}