{"url":"/dataset/atlas-v2-0","name":"ATLAS v2.0","full_name":"Anatomical Tracings of Lesions After Stroke Dataset version 2.0","description_markdown":"Accurate lesion segmentation is critical in stroke rehabilitation research for the quantification of lesion burden and accurate image processing. Current automated lesion segmentation methods for T1-weighted (T1w) MRIs, commonly used in rehabilitation research, lack accuracy and reliability. Manual segmentation remains the gold standard, but it is time-consuming, subjective, and requires significant neuroanatomical expertise. However, many methods developed with ATLAS v1.2 report low accuracy, are not publicly accessible or are improperly validated, limiting their utility to the field. Here we present ATLAS v2.0 (N=1271), a larger dataset of T1w stroke MRIs and manually segmented lesion masks that includes training (public. n=655), test (masks hidden, n=300), and generalizability (completely hidden, n=316) data. Algorithm development using this larger sample should lead to more robust solutions, and the hidden test and generalizability datasets allow for unbiased performance evaluation via segmentation challenges. We anticipate that ATLAS v2.0 will lead to improved algorithms, facilitating large-scale stroke rehabilitation research.\r\n\r\nOfficial Paper: https://www.nature.com/articles/s41597-022-01401-7","description_withheld":null,"homepage":"https://atlas.grand-challenge.org/","introduced_date":"2022-06-19","introduced_date_note":null,"introduced_by":null,"license":{"name":"Creative Commons Attribution 4.0 International License","url":null},"modalities":[{"name":"3D","url":"/datasets/modality/3d"},{"name":"Biomedical","url":"/datasets/modality/biomedical"},{"name":"Medical","url":"/datasets/modality/medical"},{"name":"MRI","url":"/datasets/modality/mri"}],"tasks":[{"name":"3D Medical Imaging Segmentation","url":"/task/3d-medical-imaging-segmentation","datasets_with_task":"/datasets/task/3d-medical-imaging-segmentation"},{"name":"Acute Stroke Lesion Segmentation","url":"/task/acute-stroke-lesion-segmentation","datasets_with_task":"/datasets/task/acute-stroke-lesion-segmentation"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["ATLAS v2.0"],"data_loaders":[],"num_papers_in_archive":8,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/acute-stroke-lesion-segmentation-on-atlas-v2","task":"Acute Stroke Lesion Segmentation","dataset_variant":"ATLAS v2.0","rows":2,"metrics":["Dice Score"],"first_row_in_archive_order":{"model":"2D U-Net Transformer","paper":"/paper/best-les-benchmarking-stroke-lesion","metrics":{"Dice Score":"0.583"},"code_links":[{"title":"prantik-pdeb/best-les","url":"https://github.com/prantik-pdeb/best-les"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/best-les-benchmarking-stroke-lesion","title":"BeSt-LeS: Benchmarking Stroke Lesion Segmentation using Deep Supervision","date":"2023-10-10","rows_on_this_dataset":2,"code_links":1,"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."}