@article{b1ae2cb2c9ba4eddb7344d660a103545,
title = "The RSNA Cervical Spine Fracture CT Dataset",
abstract = "This dataset is composed of cervical spine CT images with annotations related to fractures; it is available at https://www.kaggle.com/competitions/rsna-2022-cervical-spine-fracture-detection/. Key Points This is, to our knowledge, the largest publicly available adult cervical spine fracture CT dataset, with contributions from 12 institutions across nine countries and six continents. This dataset includes medical images, segmentations, and expert annotations from a large cohort of radiologists with subspecialist expertise in spine imaging. This dataset was used successfully for the Radiological Society of North America 2022 Cervical Spine Fracture Detection competition hosted on the Kaggle machine learning platform. The dataset is made freely available to the research community for noncommercial use.",
keywords = "CT, Diagnosis, Feature Detection, Head/Neck, Informatics, Segmentation, Spine",
author = "Lin, \{Hui Ming\} and Errol Colak and Tyler Richards and Kitamura, \{Felipe C.\} and Prevedello, \{Luciano M.\} and Jason Talbott and Ball, \{Robyn L.\} and Ekim Gumeler and Yeom, \{Kristen W.\} and Mohammad Hamghalam and Simpson, \{Amber L.\} and Jasna Strika and Deniz Bulja and Salita Angkurawaranon and Almudena P{\'e}rez-Lara and G{\'o}mez-Alonso, \{Mar{\'i}a Isabel\} and Jim{\'e}nez, \{Johanna Ortiz\} and Peoples, \{Jacob J.\} and Meng Law and Hakan Dogan and Emre Altinmakas and Ayda Youssef and Yasser Mahfouz and Jayashree Kalpathy-Cramer and Flanders, \{Adam E.\} and \{for the RSNA-ASSR-ASNR Annotators and the Dataset Curation Contributors\}",
note = "Funding Information: Machine Learning Steering Committee member and the Society for Imaging Informatics in Medicine Machine Learning Education Subcommittee (both unpaid). L.M.P. Associate editor for Radiology: Artificial Intelligence; patents planned, issued, or pending: US-20220051060-A1, “Methods for creating privacy-protecting synthetic data leveraging a constrained generative ensemble model,” and US-20220051402-A1, “Systems for automated lesion detection and related methods.” J.T. Provided expert witness deposition for Phillips, Spallas, \& Angstadt in October 2022, unrelated to this article. R.L.B. Support from RSNA to author. E.G. No relevant relationships. K.W.Y. No relevant relationships. M.H. No relevant relationships. A.L.S. Author{\textquoteright}s lab receives funding from the National Institutes of Health (NIH); member of the advisory board for the National Cancer Institute Imaging Data Commons. J.S. No relevant relationships. D.B. No relevant relationships. S.A. No relevant relationships. A.P.L. No relevant relationships. M.I.G.A. No relevant relationships. J.O.J. No relevant relationships. J.J.P. Author{\textquoteright}s lab receives funding from the NIH, which pays this author{\textquoteright}s salary. M.L. No relevant relationships. H.D. No relevant relationships. E.A. No relevant relationships. A.Y. No relevant relationships. Y.M. No relevant relationships. J.K.C. Grants or contracts from GE HealthCare and Genentech; technology licensed to Boston AI; consulting fees from Siloam Vision; deputy editor of Radiology: Artificial Intelligence. A.E.F. Standing director, liaison for information technology, of RSNA board of directors; member of RSNA News editorial board. Funding Information: E.C. supported by the Odette Professorship in Artificial Intelligence for Medical Imaging, St Michael{\textquoteright}s Hospital, Unity Health Toronto. M.H., A.L.S., and J.J.P. supported in part by the National Institutes of Health (grant R01 CA233888). Publisher Copyright: {\textcopyright} RSNA, 2023.",
year = "2023",
month = sep,
doi = "10.1148/ryai.230034",
language = "English",
volume = "5",
journal = "Radiology: Artificial Intelligence",
issn = "2638-6100",
publisher = "Radiological Society of North America (RSNA)",
number = "5",
}