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MIDRC-RICORD-1a
创建来自Hello Dataset / Robert
概要
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Overview

The COVID-19 pandemic is a global healthcare emergency. Prediction models for COVID-19 imaging are rapidly being developed to support medical decision making in imaging. However, inadequate availability of a diverse annotated dataset has limited the performance and generalizability of existing models.

MIDRC-RICORD dataset 1a was created through a collaboration between the RSNA and the Society of Thoracic Radiology (STR). Pixel-level volumetric segmentation with clinical annotations by thoracic radiology subspecialists was performed for all COVID positive thoracic computed tomography (CT) imaging studies in a labeling schema coordinated with other international consensus panels and COVID data annotation efforts.

Data Annotation

Data Abstract

120 Chest CT examinations (axial series only, any protocol).

Annotations comprised of

a) Detailed segmentation of affected regions;

b) Image-level labels (Infectious opacity, Infectious TIB/micronodules, Infectious cavity, Noninfectious nodule/mass, Atelectasis, Other noninfectious opacity)

c) Exam-level diagnostic labels (Typical, Indeterminate, Atypical, Negative for pneumonia, Halo sign, Reversed halo sign, Reticular pattern w/o parenchymal opacity, Perilesional vessel enlargement, Bronchial wall thickening, Bronchiectasis, Subpleural curvilinear line, Effusion, Pleural thickening, Pneumothorax, Pericardial effusion, Lymphadenopathy, Pulmonary embolism, Normal lung, Infectious lung disease, Emphysema, Oncologic lung disease, Non-infectious inflammatory lung disease, Non-infectious interstitial, Fibrotic lung disease, Other lung disease)

d) Exam-level procedure labels (With IV contrast, Without IV contrast, QA- inadequate motion/breathing, QA- inadequate insufficient inspiration, QA- inadequate low resolution, QA- inadequate incomplete lungs, QA- inadequate wrong body part/modality, Endotracheal tube, Central venous/arterial line, Nasogastric tube, Sternotomy wires, Pacemaker, Other support apparatus).

Supporting clinical variables: MRN*, Age, Study Date*, Exam Description, Sex, Study UID*, Image Count, Modality, Testing Result, Specimen Source (* pseudonymous values).

Citation

@misc{TCIA COVID-19 Datasets,
author={Tsai E and Simpson S and Lungren M.P and Hershman M and Roshkovan L and Colak E and
Erickson B.J and Shih G and Stein A and Kalpathy-Cramer J and Shen J and Hafez M.A.F and
John S and Rajiah P and Pogatchnik B.P and Mongan J.T and Altinmakas E and Ranschaert E
and Kitamura F.C and Topff L and Moy L and Kanne J.P and Wu C},
title={Data from the Medical Imaging Data Resource Center -
RSNA International COVID Radiology Database Release 1a - Chest CT Covid+ (MIDRC-RICORD-1a)},
year={2020},
howpublished= {\url{https://doi.org/10.7937/VTW4-X588}}
}
@Article{Clark2013,
author={Clark, Kenneth and Vendt, Bruce and Smith, Kirk and Freymann, John and Kirby, Justin
and Koppel, Paul and Moore, Stephen and Phillips, Stanley and Maffitt, David
and Pringle, Michael and Tarbox, Lawrence and Prior, Fred},
title={The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository},
journal={Journal of Digital Imaging},
year={2013},
month={Dec},
day={01},
volume={26},
number={6},
pages={1045-1057},
issn={1618-727X},
doi={10.1007/s10278-013-9622-7},
url={https://doi.org/10.1007/s10278-013-9622-7}
}

License

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数据集信息
应用场景Medical
标注类型ClassificationInstance Segmentation 2D
LicenseCustom
更新时间2021-03-24 22:52:05
数据概要
数据格式DICOM
数据数量0
文件大小7MB
标注数量0
版权归属方
The Cancer Imaging Archive (TCIA)
标注方
未知
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