The RSNA Cervical Spine Fracture CT Dataset

RSNA-ASSR-ASNR Annotators and the Dataset Curation Contributors1

doi:10.1148/ryai.230034

The RSNA Cervical Spine Fracture CT Dataset

RSNA-ASSR-ASNR Annotators and the Dataset Curation Contributors1

Icahn School of Medicine at Mount Sinai

Research output: Contribution to journal › Article › peer-review

8 Scopus citations

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.

Original language	English
Article number	e230034
Journal	Radiology: Artificial Intelligence
Volume	5
Issue number	5
DOIs	https://doi.org/10.1148/ryai.230034
State	Published - Sep 2023

Keywords

CT
Diagnosis
Feature Detection
Head/Neck
Informatics
Segmentation
Spine

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10.1148/ryai.230034

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@article{8857201a68834ee087553fec0e47651c,

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 = "{RSNA-ASSR-ASNR Annotators and the Dataset Curation Contributors1} and 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.}",

note = "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 Inc.",

number = "5",

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AU - RSNA-ASSR-ASNR Annotators and the Dataset Curation Contributors1

AU - Lin, Hui Ming

AU - Colak, Errol

AU - Richards, Tyler

AU - Kitamura, Felipe C.

AU - Prevedello, Luciano M.

AU - Talbott, Jason

AU - Ball, Robyn L.

AU - Gumeler, Ekim

AU - Yeom, Kristen W.

AU - Hamghalam, Mohammad

AU - Simpson, Amber L.

AU - Strika, Jasna

AU - Bulja, Deniz

AU - Angkurawaranon, Salita

AU - Pérez-Lara, Almudena

AU - Gómez-Alonso, María Isabel

AU - Jiménez, Johanna Ortiz

AU - Peoples, Jacob J.

AU - Law, Meng

AU - Dogan, Hakan

AU - Altinmakas, Emre

AU - Youssef, Ayda

AU - Mahfouz, Yasser

AU - Kalpathy-Cramer, Jayashree

AU - Flanders, Adam E.

PY - 2023/9

Y1 - 2023/9

N2 - 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.

AB - 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.

KW - CT

KW - Diagnosis

KW - Feature Detection

KW - Head/Neck

KW - Informatics

KW - Segmentation

KW - Spine

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The RSNA Cervical Spine Fracture CT Dataset

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Keywords

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