Published October 25, 2021
| Version v1.0
Dataset
Open
Covid-19 CT dataset for Body Part Regression Tutorial
Authors/Creators
Description
The dataset is a subset of CT scans from the Covid-19-AR dataset from the Cancer Image Archive. The data were converted from the DICOM file format to the NifTI file format for better and easier handling. The converted dataset was created for a tutorial of the bpreg python package.
Acknowledgment:
The dataset was funded with federal funds from the National Center for Advancing Translational Sciences UL1 TR003107 and the National Cancer Institute, Contract No. 75N91019D00024, Subcontract 20X023F.
Files
covid-19-ar-ct-scans-nifti.zip
Files
(7.1 GB)
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md5:4eb75b9d6b616882968d40c9f32b7106
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Additional details
References
- Desai, S., Baghal, A., Wongsurawat, T., Al-Shukri, S., Gates, K., Farmer, P., Rutherford, M., Blake, G.D., Nolan, T., Powell, T., Sexton, K., Bennett, W., Prior, F. (2020). Data from Chest Imaging with Clinical and Genomic Correlates Representing a Rural COVID-19 Positive Population [Data set]. The Cancer Imaging Archive. DOI: https://doi.org/10.7937/tcia.2020.py71-5978.
- Desai, S., Baghal, A., Wongsurawat, T., Jenjaroenpun, P., Powell, T., Al-Shukri, S., Gates, K., Farmer, P., Rutherford, M., Blake, G., Nolan, T., Sexton, K., Bennett, W., Smith, K., Syed, S., Prior, F. (2020). Chest imaging representing a COVID-19 positive rural U.S. population. Scientific Data. 2020;7(1):414. doi: https://doi.org/10.1038/s41597-020-00741-6.
- Jenjaroenpun, P., Wanchai, V., OnoMoore, K.D., Laudadio, J., James, L.P., Adams, S.H., Prior, F., Nookaew, I., Ussery, D.W., Wongsurawat, T. (2020). Two SARS-CoV-2 genome sequences of isolates from rural U.S. patients harboring the D614G mutation, obtained using Nanopore sequencing. Microbiology Resource Announcements, 2020. DOI: 10.1128/MRA.01109-20.
- Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. 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: 10.1007/s10278-013-9622-7