{"url":"/dataset/lits17","name":"LiTS17","full_name":"Liver Tumor Segmentation Challenge 2017","description_markdown":"**LiTS17** is a liver tumor segmentation benchmark. The data and segmentations are provided by various clinical sites around the world. The training data set contains 130 CT scans and the test data set 70 CT scans.\r\nImage Source: [https://arxiv.org/pdf/1707.07734.pdf](https://arxiv.org/pdf/1707.07734.pdf)","description_withheld":null,"homepage":"https://competitions.codalab.org/competitions/17094","introduced_date":"2019-01-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/the-liver-tumor-segmentation-benchmark-lits","title":"The Liver Tumor Segmentation Benchmark (LiTS)","first_author":"Patrick Bilic","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Medical","url":"/datasets/modality/medical"}],"tasks":[{"name":"Medical Image Segmentation","url":"/task/medical-image-segmentation","datasets_with_task":"/datasets/task/medical-image-segmentation"},{"name":"Tumor Segmentation","url":"/task/tumor-segmentation","datasets_with_task":"/datasets/task/tumor-segmentation"},{"name":"Computed Tomography (CT)","url":"/task/computed-tomography-ct","datasets_with_task":"/datasets/task/computed-tomography-ct"},{"name":"Liver Segmentation","url":"/task/liver-segmentation","datasets_with_task":"/datasets/task/liver-segmentation"}],"languages":[{"name":"Chinese","url":"/datasets/language/chinese"}],"variants":["LiTS2017","LiTS17"],"data_loaders":[{"repo":"https://github.com/pytorch/tutorials","url":"https://github.com/pytorch/tutorials","frameworks":["pytorch"]}],"num_papers_in_archive":45,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/liver-segmentation-on-lits2017","task":"Liver Segmentation","dataset_variant":"LiTS2017","rows":9,"metrics":["IoU","Dice","HD"],"first_row_in_archive_order":{"model":"Polar U-Net","paper":"/paper/training-on-polar-image-transformations","metrics":{"Dice":"93.02","IoU":"89.85"},"code_links":[{"title":"marinbenc/medical-polar-training","url":"https://github.com/marinbenc/medical-polar-training"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/medical-image-segmentation-on-lits2017","task":"Medical Image Segmentation","dataset_variant":"LiTS2017","rows":2,"metrics":["Dice"],"first_row_in_archive_order":{"model":"UNet 3+","paper":"/paper/unet-3-a-full-scale-connected-unet-for","metrics":{"Dice":"0.9675"},"code_links":[{"title":"yingkaisha/keras-unet-collection","url":"https://github.com/yingkaisha/keras-unet-collection"},{"title":"ZJUGiveLab/UNet-Version","url":"https://github.com/ZJUGiveLab/UNet-Version"},{"title":"mindspore-ai/models","url":"https://github.com/mindspore-ai/models/tree/master/research/cv/Neighbor2Neighbor"},{"title":"DaloroAT/first_break_picking","url":"https://github.com/DaloroAT/first_break_picking"},{"title":"hamidriasat/UNet-3-Plus","url":"https://github.com/hamidriasat/UNet-3-Plus"},{"title":"Owais-Ansari/Unet3plus","url":"https://github.com/Owais-Ansari/Unet3plus"},{"title":"MindSpore-scientific-2/code-5","url":"https://github.com/MindSpore-scientific-2/code-5/tree/main/D-Unet_Mindspore"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/tumor-segmentation-on-lits17","task":"Tumor Segmentation","dataset_variant":"LiTS17","rows":1,"metrics":["Dice","NSD"],"first_row_in_archive_order":{"model":"label-free","paper":"/paper/label-free-liver-tumor-segmentation","metrics":{"Dice":"59.77","NSD":"61.29"},"code_links":[{"title":"mrgiovanni/synthetictumors","url":"https://github.com/mrgiovanni/synthetictumors"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/ct-liver-segmentation-via-pvt-based-encoding","title":"CT Liver Segmentation via PVT-based Encoding and Refined Decoding","date":"2024-01-17","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/label-free-liver-tumor-segmentation","title":"Label-Free Liver Tumor Segmentation","date":"2023-03-27","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/training-on-polar-image-transformations","title":"Training on Polar Image Transformations Improves Biomedical Image Segmentation","date":"2021-09-29","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/kiu-net-overcomplete-convolutional","title":"KiU-Net: Overcomplete Convolutional Architectures for Biomedical Image and Volumetric Segmentation","date":"2020-10-04","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/learning-semantics-enriched-representation","title":"Learning Semantics-enriched Representation via Self-discovery, Self-classification, and Self-restoration","date":"2020-07-14","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/unet-3-a-full-scale-connected-unet-for","title":"UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation","date":"2020-04-19","rows_on_this_dataset":2,"code_links":7,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":17,"samples_ran":4,"samples_unverified":13,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"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/190503639","title":"Liver Lesion Segmentation with slice-wise 2D Tiramisu and Tversky loss function","date":"2019-05-09","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/h-denseunet-hybrid-densely-connected-unet-for","title":"H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation from CT Volumes","date":"2017-09-21","rows_on_this_dataset":2,"code_links":2,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":1,"samples_harvested":17,"samples_ran":4,"samples_unverified":13,"pointer_only_for_licence":1,"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."}