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Updated: Nov 23, 2025
Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
Published on: December 19, 2020
The RSNA International COVID-19 Open Radiology Database (RICORD)
Emily B Tsai1, Scott Simpson1, Matthew P Lungren1
1From the Department of Radiology, Stanford University, Stanford, Calif (E.B.T., J.S., B.P.P.); Department of Radiology, University of Pennsylvania Hospital, Philadelphia, Pa (S.S., M. Hershman, L.R.); Department of Radiology, Stanford University School of Medicine, Stanford University Medical Center, 725 Welch Rd, Room 1675, Stanford, CA 94305-5913 (M.P.L.); Department of Medical Imaging, University of Toronto, Unity Health Toronto, Toronto, Canada (E.C.); Department of Radiology, Mayo Clinic, Rochester, Minn (B.J.E., P.R.); Department of Radiology, Weill Cornell Medicine, New York, NY (G.S.); MD.ai, New York, NY (A.S.); Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, Mass (J.K.C.); Department of Diagnostic and Interventional Radiology, Cairo University Kasr Alainy Faculty of Medicine, Cairo, Egypt (M. Hafez); Department of Radiology, The Ottawa Hospital, Ottawa, Canada (S.J.); Department of Radiology and Biomedical Imaging, Center for Intelligent Imaging, San Francisco, Calif (J.M.); Department of Radiology, Koç University School of Medicine, Koç University Hospital, Istanbul, Turkey (E.A.); Department of Radiology, ETZ Hospital, Tilburg, the Netherlands (E.R.R.); Department of Radiology, University of Ghent, Ghent, Belgium (E.R.R.); Department of Diagnostic Imaging, Universidade Federal de São Paulo, São Paulo, Brazil (F.C.K.); Department of Radiology, Netherlands Cancer Institute, Amsterdam, the Netherlands (L.T.); Department of Radiology, NYU Grossman School of Medicine, Center for Advanced Imaging Innovation and Research, Laura and Isaac Perlmutter Cancer Center, New York, NY (L.M.); Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wis (J.P.K.); and Department of Thoracic Imaging, University of Texas MD Anderson Cancer Center, Houston, Tex (C.C.W.).
Abstract:
The coronavirus disease 2019 (COVID-19) pandemic is a global health care emergency. Although reverse-transcription polymerase chain reaction testing is the reference standard method to identify patients with COVID-19 infection, chest radiography and CT play a vital role in the detection and management of these patients. Prediction models for COVID-19 imaging are rapidly being developed to support medical decision making. However, inadequate availability of a diverse annotated data set has limited the performance and generalizability of existing models. To address this unmet need, the RSNA and Society of Thoracic Radiology collaborated to develop the RSNA International COVID-19 Open Radiology Database (RICORD). This database is the first multi-institutional, multinational, expert-annotated COVID-19 imaging data set. It is made freely available to the machine learning community as a research and educational resource for COVID-19 chest imaging. Pixel-level volumetric segmentation with clinical annotations was performed by thoracic radiology subspecialists for all COVID-19-positive thoracic CT scans. The labeling schema was coordinated with other international consensus panels and COVID-19 data annotation efforts, the European Society of Medical Imaging Informatics, the American College of Radiology, and the American Association of Physicists in Medicine. Study-level COVID-19 classification labels for chest radiographs were annotated by three radiologists, with majority vote adjudication by board-certified radiologists. RICORD consists of 240 thoracic CT scans and 1000 chest radiographs contributed from four international sites. It is anticipated that RICORD will ideally lead to prediction models that can demonstrate sustained performance across populations and health care systems.
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