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. 2023 Aug 30;5(5):e230034.
doi: 10.1148/ryai.230034. eCollection 2023 Sep.

The RSNA Cervical Spine Fracture CT Dataset

Hui Ming Lin  1 , Errol Colak  1 , Tyler Richards  1 , Felipe C Kitamura  1 , Luciano M Prevedello  1 , Jason Talbott  1 , Robyn L Ball  1 , Ekim Gumeler  1 , Kristen W Yeom  1 , Mohammad Hamghalam  1 , Amber L Simpson  1 , Jasna Strika  1 , Deniz Bulja  1 , Salita Angkurawaranon  1 , Almudena Pérez-Lara  1 , María Isabel Gómez-Alonso  1 , Johanna Ortiz Jiménez  1 , Jacob J Peoples  1 , Meng Law  1 , Hakan Dogan  1 , Emre Altinmakas  1 , Ayda Youssef  1 , Yasser Mahfouz  1 , Jayashree Kalpathy-Cramer  1 , Adam E Flanders  1 ; RSNA-ASSR-ASNR Annotators and the Dataset Curation Contributors
Affiliations

The RSNA Cervical Spine Fracture CT Dataset

Hui Ming Lin et al. Radiol Artif Intell. .

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/.

Keywords: CT; Diagnosis; Feature Detection; Head/Neck; Informatics; Segmentation; Spine.

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Conflict of interest statement

Disclosures of conflicts of interest: H.M.L. No relevant relationships. E.C. No relevant relationships. T.R. No relevant relationships. F.C.K. Consultant for MD.ai and GE HealthCare; member of the Radiological Society of North America (RSNA) 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’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’s lab receives funding from the NIH, which pays this author’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.

Figures

Axial noncontrast cervical spine CT image with bounding boxes surrounding the fractured vertebrae, annotated by individual neuroradiologists (red). Ground truth bounding box (cyan) was calculated by taking the largest sum of all individual bounding boxes, representing the largest bounding box.
Figure 1:
Axial noncontrast cervical spine CT image with bounding boxes surrounding the fractured vertebrae, annotated by individual neuroradiologists (red). Ground truth bounding box (cyan) was calculated by taking the largest sum of all individual bounding boxes, representing the largest bounding box.
Example of cervical spine segmentation, with each color representing different vertebrae levels. (A) Image illustrates the segmentations generated in the sagittal plane. (B) Image depicts the segmentation mask overlaying the corresponding reconstructed sagittal DICOM section. The sagittal segmentation can be flipped onto the (C) axial plane, which produces (D) the segmentation corresponding to the original axial DICOM images. DICOM = Digital Imaging and Communications in Medicine.
Figure 2:
Example of cervical spine segmentation, with each color representing different vertebrae levels. (A) Image illustrates the segmentations generated in the sagittal plane. (B) Image depicts the segmentation mask overlaying the corresponding reconstructed sagittal DICOM section. The sagittal segmentation can be flipped onto the (C) axial plane, which produces (D) the segmentation corresponding to the original axial DICOM images. DICOM = Digital Imaging and Communications in Medicine.
See this image and copyright information in PMC

References

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