{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/the-kits19-challenge-data-300-kidney-tumor","title":"The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical Outcomes","arxiv_id":"1904.00445","date":"2019-03-31","proceeding":null,"authors":["Nicholas Heller","Niranjan Sathianathen","Arveen Kalapara","Edward Walczak","Keenan Moore","Heather Kaluzniak","Joel Rosenberg","Paul Blake","Zachary Rengel","Makinna Oestreich","Joshua Dean","Michael Tradewell","Aneri Shah","Resha Tejpaul","Zachary Edgerton","Matthew Peterson","Shaneabbas Raza","Subodh Regmi","Nikolaos Papanikolopoulos","Christopher Weight"],"abstract":"The morphometry of a kidney tumor revealed by contrast-enhanced Computed Tomography (CT) imaging is an important factor in clinical decision making surrounding the lesion's diagnosis and treatment. Quantitative study of the relationship between kidney tumor morphology and clinical outcomes is difficult due to data scarcity and the laborious nature of manually quantifying imaging predictors. Automatic semantic segmentation of kidneys and kidney tumors is a promising tool towards automatically quantifying a wide array of morphometric features, but no sizeable annotated dataset is currently available to train models for this task. We present the KiTS19 challenge dataset: A collection of multi-phase CT imaging, segmentation masks, and comprehensive clinical outcomes for 300 patients who underwent nephrectomy for kidney tumors at our center between 2010 and 2018. 210 (70%) of these patients were selected at random as the training set for the 2019 MICCAI KiTS Kidney Tumor Segmentation Challenge and have been released publicly. With the presence of clinical context and surgical outcomes, this data can serve not only for benchmarking semantic segmentation models, but also for developing and studying biomarkers which make use of the imaging and semantic segmentation masks.","url_abs":"https://arxiv.org/abs/1904.00445v2","url_pdf":"https://arxiv.org/pdf/1904.00445v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"the-kits19-challenge-data-300-kidney-tumor","repo_url":"https://github.com/neheller/kits19","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"the-kits19-challenge-data-300-kidney-tumor","repo_url":"https://github.com/BecauseOfGAN/capston","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"the-kits19-challenge-data-300-kidney-tumor","repo_url":"https://github.com/BecauseOfGAN/capstone","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"the-kits19-challenge-data-300-kidney-tumor","repo_url":"https://github.com/KrissHs/KIT19","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"the-kits19-challenge-data-300-kidney-tumor","repo_url":"https://github.com/Stsh4lson/TOM-2020","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"the-kits19-challenge-data-300-kidney-tumor","repo_url":"https://github.com/kirangpcet/KRCCTumor","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"the-kits19-challenge-data-300-kidney-tumor","repo_url":"https://github.com/michal-crosta/GIT19","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"benchmarking","task_name":"Benchmarking"},{"task_slug":"computed-tomography-ct","task_name":"Computed Tomography (CT)"},{"task_slug":"decision-making","task_name":"Decision Making"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"tumor-segmentation","task_name":"Tumor Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1904.00445","atlas_url":"https://app.syntology.ai/?focus=1904.00445","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.00445"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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