{"url":"/dataset/medical-segmentation-decathlon","name":"Medical Segmentation Decathlon","full_name":null,"description_markdown":"The Medical Segmentation Decathlon is a collection of medical image segmentation datasets. It contains a total of 2,633 three-dimensional images collected across multiple anatomies of interest, multiple modalities and multiple sources. Specifically, it contains data for the following body organs or parts: Brain, Heart, Liver, Hippocampus, Prostate, Lung, Pancreas, Hepatic Vessel, Spleen and Colon.\r\n\r\nSource: [A large annotated medical image dataset for the development and evaluation of segmentation algorithms](https://arxiv.org/pdf/1902.09063.pdf)\r\nImage Source: [Simpson et al](https://arxiv.org/pdf/1902.09063.pdf)","description_withheld":null,"homepage":"http://medicaldecathlon.com/","introduced_date":"2019-02-25","introduced_date_note":null,"introduced_by":{"paper":"/paper/a-large-annotated-medical-image-dataset-for","title":"A large annotated medical image dataset for the development and evaluation of segmentation algorithms","first_author":"Amber L. Simpson","url":null},"license":{"name":"CC-BY-SA 4.0","url":"https://creativecommons.org/licenses/by-sa/4.0/"},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Medical","url":"/datasets/modality/medical"}],"tasks":[{"name":"Semantic Segmentation","url":"/task/semantic-segmentation","datasets_with_task":"/datasets/task/semantic-segmentation"},{"name":"Medical Image Segmentation","url":"/task/medical-image-segmentation","datasets_with_task":"/datasets/task/medical-image-segmentation"},{"name":"Image Registration","url":"/task/image-registration","datasets_with_task":"/datasets/task/image-registration"}],"languages":[],"variants":["Medical Segmentation Decathlon"],"data_loaders":[{"repo":null,"url":"","frameworks":["pytorch"]}],"num_papers_in_archive":97,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/medical-image-segmentation-on-medical","task":"Medical Image Segmentation","dataset_variant":"Medical Segmentation Decathlon","rows":5,"metrics":["Dice (Average)","NSD"],"first_row_in_archive_order":{"model":"Swin UNETR","paper":"/paper/self-supervised-pre-training-of-swin","metrics":{"Dice (Average)":"78.68","NSD":"89.28"},"code_links":[{"title":"Project-MONAI/research-contributions","url":"https://github.com/Project-MONAI/research-contributions/tree/master/SwinUNETR"},{"title":"jusiro/fewshot-finetuning","url":"https://github.com/jusiro/fewshot-finetuning"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/self-supervised-pre-training-of-swin","title":"Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis","date":"2021-11-29","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/dints-differentiable-neural-network-topology","title":"DiNTS: Differentiable Neural Network Topology Search for 3D Medical Image Segmentation","date":"2021-03-29","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/transferable-visual-words-exploiting-the","title":"Transferable Visual Words: Exploiting the Semantics of Anatomical Patterns for Self-supervised Learning","date":"2021-02-21","rows_on_this_dataset":1,"code_links":4,"syntology":null},{"paper":"/paper/models-genesis-generic-autodidactic-models","title":"Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis","date":"2019-08-19","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/nnu-net-self-adapting-framework-for-u-net","title":"nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation","date":"2018-09-27","rows_on_this_dataset":1,"code_links":9,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":11,"samples_ran":3,"samples_unverified":8,"pointer_only_for_licence":0,"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":1,"samples_harvested":11,"samples_ran":3,"samples_unverified":8,"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."}