{"url":"/dataset/miccai-2015-head-and-neck-challenge","name":"MICCAI 2015 Head and Neck Challenge","full_name":null,"description_markdown":"This database is provided and maintained by Dr. Gregory C Sharp (Harvard Medical School – MGH, Boston) and his group.\r\n\r\nThe data here provided have been used for the “Head and Neck Auto Segmentation MICCAI Challenge (2015)”.\r\nTo cite the challenge or the data, please refer to:\r\nRaudaschl, P. F., Zaffino, P., Sharp, G. C., Spadea, M. F., Chen, A., Dawant, B. M., … & Jung, F. (2017).\r\nEvaluation of segmentation methods on head and neck CT: Auto‐segmentation challenge 2015.\r\nMedical Physics, 44(5), 2020-2036.\r\n\r\nPDDCA version 1.4.1 comprises 48 patient CT images from the Radiation Therapy Oncology Group (RTOG) 0522 study (a multi-institutional clinical trial led by Dr Kian Ang), together with manual segmentation of left and right parotid glands, brainstem, optic chiasm, optic nerves (both left and right), mandible, submandibular glands (both left and right) and manual identification of bony landmarks.\r\nWe give this data to the community in the hopes that it will be helpful. Any errors in delineation and markup are ours, and are not the fault of participating doctors.\r\nPlease see pddca.odt for complete information.\r\nFor practical reasons the database is split in 3 zipped files.\r\nPart 3 contains images with some of the above listed structures missing.\r\n\r\nDetails regarding the Challenge Train/Test Splits can be found in the dataset description","description_withheld":null,"homepage":"https://www.imagenglab.com/newsite/pddca/","introduced_date":"2016-04-01","introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Medical Image Segmentation","url":"/task/medical-image-segmentation","datasets_with_task":"/datasets/task/medical-image-segmentation"}],"languages":[],"variants":["MICCAI 2015 Head and Neck Challenge"],"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/medical-image-segmentation-on-miccai-2015-2","task":"Medical Image Segmentation","dataset_variant":"MICCAI 2015 Head and Neck Challenge","rows":1,"metrics":["Dice"],"first_row_in_archive_order":{"model":"AnatomyNet","paper":"/paper/anatomynet-deep-learning-for-fast-and-fully","metrics":{"Dice":"79.25"},"code_links":[{"title":"wentaozhu/AnatomyNet-for-anatomical-segmentation","url":"https://github.com/wentaozhu/AnatomyNet-for-anatomical-segmentation"},{"title":"BioWar/Satellite-Image-Segmentation-using-Deep-Learning-for-Deforestation-Detection","url":"https://github.com/BioWar/Satellite-Image-Segmentation-using-Deep-Learning-for-Deforestation-Detection"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/anatomynet-deep-learning-for-fast-and-fully","title":"AnatomyNet: Deep Learning for Fast and Fully Automated Whole-volume Segmentation of Head and Neck Anatomy","date":"2018-08-15","rows_on_this_dataset":1,"code_links":2,"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."}