The Cancer Imaging Archive (TCIA) Public Access

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locationhttps://doi.org/10.7937/TCIA.2020.GQRY-NC81

Summary

Excerpt

Image RemovedImage ModifiedThis These retrospective NIfTI image dataset datasets consists of unenhanced chest CTs: 

  • First dataset - from 632 patients with COVID-19 infections at initial point of care, and
  • Second dataset - a second set of 121 CTs from 29 patients with COVID-19 infections with serial / sequential CTs.

The initial images for both datasets were acquired at acquired at the point of care in an outbreak setting from setting from patients with Reverse Transcription Polymerase Chain Reaction (RT-PCR) confirmation for the presence of SARS-CoV-2.

Image AddedPatients presented to a health care setting with a combination of symptoms, exposure to an infected patient, or travel history to an outbreak region. All patients had a positive RT-PCR for SARS-CoV-2 from a sample obtained within 1 day of the initial CT.    CT exams were performed without intravenous contrast and with a soft tissue reconstruction algorithm.  The DICOM images were subsequently converted into NIfTI format. . The second dataset also had other follow up CTs, in addition to the initial point of care CT.

A multidisciplinary team trained several models using portions of this TCIA data setthe first dataset, along with additional CTs and manually annotated images from other sources. A classification model derived in part from this data the first dataset is described in a Nature Communications manuscript at:  https://doi.org/10.1038/s41467-020-17971-2.  The NVIDIA-related frameworks and models specific to this publication are available at no cost as part of the NVIDIA Clara Train SDK at https://ngc.nvidia.com/catalog/containers/nvidia:clara:ai-covid-19. This includes both inference-based pipelines for evaluation, as well as model weights for further training or fine tuning in outside institutions.  In addition, a web-based version of this model (for research use only) with drag-and-drop functionality for evaluating individual scans can be found at The second data set of 121 serial / sequential CTs in 29 patients is reported in a Scientific Reports manuscript at  https://marketplacedoi.arterysorg/10.com/model/nvidiacovidCT.  Uploading a CT yields a return email that contains results. 1038/s41598-021-85694-5. 

Acknowledgements

The Imaging AI in COVID team would like to acknowledge the following individuals who supported this multi-disciplinary multi-national team effort:

  • All frontline workers and Peng An, Sheng Xu, Evrim B. Turkbey, Stephanie A. Harmon, Thomas H. Sanford, Amel Amalou, Michael Kassin, Nicole Varble, Maxime Blain, Dilara Long, Dima Hammoud, Ashkan Malayeri, Elizabeth Jones, Holger Roth, Ziyue Xu, Dong Yang, Andriy Myronenko, Victoria Anderson, Mona Flores, Francesca Patella, Maurizio Cariati, Kaku Tamura, Hirofumi Obinata, Hitoshi Mori, Ulas Bagci, Daguang Xu, Hayet Amalou, Robert Suh, Gianpaolo Carrafiello, Baris Turkbey, Bradford J. Wood.
  • Thanks for leadership support to:  John Gallin, Steve Holland, Cliff Lane, Bruce Tromberg, Tom Misteli, Bill Dahut.
  • Supported by the NIH Center for Interventional Oncology and the NIH Intramural Targeted Anti-COVID-19 (ITAC) Program.

TCIA COVID-19 Datasets

Additional datasets and information about TCIA efforts to support COVID-19 research can be found here.

Localtab Group


Localtab
activetrue
titleData Access

Data Access

appbox.com/s/t7h7xfdaqu3w9uptnvxze38i4ptp94ml
Data TypeDownload all or Query/FilterLicense

Images (NIfTI, 12.71 GB)

First dataset


Tcia button generator
urlhttps://faspex.cancerimagingarchive.net/aspera/faspex/public/package?context=eyJyZXNvdXJjZSI6InBhY2thZ2VzIiwidHlwZSI6ImV4dGVybmFsX2Rvd25sb2FkX3BhY2thZ2UiLCJpZCI6IjQ1OSIsInBhc3Njb2RlIjoiYWI5ZjliMGUwYjY4MmQ2OGM2MzMxZDFhY2JjNGNiZTViMThhYWEwMiIsInBhY2thZ2VfaWQiOiI0NTkiLCJlbWFpbCI6ImhlbHBAY2FuY2VyaW1hZ2luZ2FyY2hpdmUubmV0In0=&redirected=true



(Download and apply the IBM-Aspera-Connect plugin to your browser to retrieve this faspex package) 

Tcia cc by 4

Images (NIfTI, 2 GB)

Second dataset


Tcia button generator
urlhttps://
faspex.
cancerimagingarchive.net/aspera/faspex/public/package?context=eyJyZXNvdXJjZSI6InBhY2thZ2VzIiwidHlwZSI6ImV4dGVybmFsX2Rvd25sb2FkX3BhY2thZ2UiLCJpZCI6IjQ2MCIsInBhc3Njb2RlIjoiMDYwYjRlZDhkYjkzZmYzZDBhZDgwMzQ4NWY0Y2Y3ODY4MzFjODljZiIsInBhY2thZ2VfaWQiOiI0NjAiLCJlbWFpbCI6ImhlbHBAY2FuY2VyaW1hZ2luZ2FyY2hpdmUubmV0In0=&redirected=true


(Download and apply the IBM-Aspera-Connect plugin to your browser to retrieve this faspex package)

Tcia cc by 4

Click the Versions tab for more info about data releases.

Please contact help@cancerimagingarchive.net  with any questions regarding usage.


Localtab
titleDetailed Description

Detailed Description

Image Statistics


Modalities

CT

Number of Patients

632661

Number of StudiesSeries

650771
Images Size (GB)12.7114.7


Link to publication below contains AI model that was only partly derived from this data, and also from other data not present here on TCIA.

  • Harmon, S. A., Sanford, T. H., Xu, S., Turkbey, E. B., Roth, H., Xu, Z., Yang, D., Myronenko, A., Anderson, V., Amalou, A., Blain, M., Kassin, M., Long, D., Varble, N., Walker, S. M., Bagci, U., Ierardi, A. M., Stellato, E., Plensich, G. G., Franceschelli, G., Girlando, C., Irmici, G., Labella, D., Hammoud, D., Malayeri, A., Jones, E., Summers, R. M., Choyke, P.L., Xu, D., Flores, M., Tamura, K., Obinata, H., Mori, H., Patella, F., Cariati, M., Carrafiello, G., An, P., Wood, B. J., & Turkbey, B. (2020). Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets. Nature Communications, 11(1). https://doi.org/10.1038/s41467-020-17971-2


Localtab
titleCitations & Data Usage Policy

Citations & Data Usage Policy

tcia-limited-license-4-internationalpolicy

Info
titleData Citation

An, P., Xu, S., Harmon SA, S. A., Turkbey EB, E. B., Sanford TH, T. H., Amalou, A., Kassin, M., Varble, N., Blain, M., Anderson, V., Patella, F., Carrafiello, G., Turkbey BT, B. T., & Wood BJ , B. J. (2020). CT Images in CovidCOVID-19 [Data set]. The Cancer Imaging Archive. DOI: https://doi.org/10.7937/tciaTCIA.2020.gqry-nc81GQRY-NC81


Info
titleAcknowledgement

The Multi-national NIH Consortium for CT AI in COVID-19.


Info
titlePublication Citation

Link to publication below contains AI model that was only partly derived from this data, but also from other data, not present here on TCIA.

Harmon SA, Sanford TH, Xu S, Turkbey EB, Roth H, Xu Z, Yang D, Myronenko A, Anderson V, Amalou A, Blain M, Kassin M, Long D, Varble N, Walker SM, Bagci U, Ierardi AM, Stellato E, Plensich GG, Franceschelli G, Girlando C, Irmici G, Labella D, Hammoud D, Malayeri A, Jones E, Summer RM, Choyke PL, Xu D, Flores M, Tamura K, Obinata H, Mori H, Patella F, Cariati M, Carrafiello G, An P, Wood BJ, Turkbey B Harmon, S. A., Sanford, T. H., Xu, S., Turkbey, E. B., Roth, H., Xu, Z., Yang, D., Myronenko, A., Anderson, V., Amalou, A., Blain, M., Kassin, M., Long, D., Varble, N., Walker, S. M., Bagci, U., Ierardi, A. M., Stellato, E., Plensich, G. G., Franceschelli, G., Girlando, C., Irmici, G., Labella, D., Hammoud, D., Malayeri, A., Jones, E., Summers, R. M., Choyke, P.L., Xu, D., Flores, M., Tamura, K., Obinata, H., Mori, H., Patella, F., Cariati, M., Carrafiello, G., An, P., Wood, B. J., & Turkbey, B. (2020). Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets. Nature Communications. DOI: , 11(1). https://doi.org/10.1038/s41467-020-17971-2


Info
titleAcknowledgement

The Multi-national NIH Consortium for CT AI in COVID-19.

Info
titleTCIA Citation

Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., Tarbox, L., & Prior, F. (2013).  The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, . Journal of Digital Imaging, Volume 26, Number (6, December, 2013, pp 1045-1057. DOI: ), 1045–1057.https://doi.org/10.1007/s10278-013-9622-7.



Other Publications Using This Data

TCIA maintains a list of publications which leverage TCIA our data. If you have a manuscript you'd like to add please contact the TCIA Helpdesk.


Localtab
titleVersions

Version

1

2 (Current): Updated 2021/05/25

Data TypeDownload all or Query/Filter

Images (NIfTI, 12.71 GB)

First dataset


Tcia button generator
urlhttps://faspex.cancerimagingarchive.net/aspera/faspex/public/package?context=eyJyZXNvdXJjZSI6InBhY2thZ2VzIiwidHlwZSI6ImV4dGVybmFsX2Rvd25sb2FkX3BhY2thZ2UiLCJpZCI6IjQ1OSIsInBhc3Njb2RlIjoiYWI5ZjliMGUwYjY4MmQ2OGM2MzMxZDFhY2JjNGNiZTViMThhYWEwMiIsInBhY2thZ2VfaWQiOiI0NTkiLCJlbWFpbCI6ImhlbHBAY2FuY2VyaW1hZ2luZ2FyY2hpdmUubmV0In0=&redirected=true



Images (NIfTI, 2 GB)

Second dataset


Tcia button generator
urlhttps://faspex.cancerimagingarchive.net/aspera/faspex/public/package?context=eyJyZXNvdXJjZSI6InBhY2thZ2VzIiwidHlwZSI6ImV4dGVybmFsX2Rvd25sb2FkX3BhY2thZ2UiLCJpZCI6IjQ2MCIsInBhc3Njb2RlIjoiMDYwYjRlZDhkYjkzZmYzZDBhZDgwMzQ4NWY0Y2Y3ODY4MzFjODljZiIsInBhY2thZ2VfaWQiOiI0NjAiLCJlbWFpbCI6ImhlbHBAY2FuY2VyaW1hZ2luZ2FyY2hpdmUubmV0In0=&redirected=true



Added second dataset, 29 patients/121 CT images.

Version 1: Updated 2020/08/31

Data TypeDownload all or Query/Filter

Images (NIfTI, 12.71 GB)


Tcia button generator
urlhttps://
app
faspex.
box.com/s/t7h7xfdaqu3w9uptnvxze38i4ptp94ml
cancerimagingarchive.net/aspera/faspex/public/package?context=eyJyZXNvdXJjZSI6InBhY2thZ2VzIiwidHlwZSI6ImV4dGVybmFsX2Rvd25sb2FkX3BhY2thZ2UiLCJpZCI6IjQ1OSIsInBhc3Njb2RlIjoiYWI5ZjliMGUwYjY4MmQ2OGM2MzMxZDFhY2JjNGNiZTViMThhYWEwMiIsInBhY2thZ2VfaWQiOiI0NTkiLCJlbWFpbCI6ImhlbHBAY2FuY2VyaW1hZ2luZ2FyY2hpdmUubmV0In0=&redirected=true