Pancreas-CT-SEG: DICOM-converted manual segmentations of pancreas for the Pancreas-CT collection
Description
This dataset contributes DICOM-converted annotations of the pancreas to the publicly available National Cancer Institute Imaging Data Commons [1] Pancreas-CT collection (https://portal.imaging.datacommons.cancer.gov/explore/filters/?collection_id=Community&collection_id=pancreas_ct). The Pancreas-CT [2] collection was initially released by The Cancer Imaging Archive (TCIA) [2,3,4]. While the images in this collection are stored in the standard DICOM format, the collection is also accompanied by 80 manual segmentations of the pancreas in the NIFTI format. Converting these NIFTI manual annotations to DICOM allows users to visualize the CT images and corresponding pancreas annotations in public DICOM-based viewers. The DICOM segmentation format includes additional metadata describing the contained segmented structure using standard SNOMED-CT terminology, and allowing users to easily map them to the reference DICOM CT images. The converted DICOM objects can be stored in any DICOM server that supports those objects (including Google Healthcare DICOM stores), and the DICOM Segmentations can be visualized using off-the-shelf tools, such as OHIF Viewer.
To visualize and explore segmentations in this collection use this link to open it in IDC Portal: https://portal.imaging.datacommons.cancer.gov/explore/filters/?analysis_results_id=Pancreas-CT-SEG.
Conversion from NIFTI to DICOM SEG modality was performed using the dcmqi toolkit (https://github.com/QIICR/dcmqi)[4]. Resulting objects were validated using dicom3tools dciodvfy (https://www.dclunie.com/dicom3tools.html). Details describing the conversion process are provided in this GitHub repository: https://github.com/ImagingDataCommons/idc-dicom-seg-conversions.
Files
Pancreas-CT-SEG.zip
Files
(1.7 MB)
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Additional details
Related works
- Is derived from
- Dataset: 10.7937/K9/TCIA.2016.tNB1kqBU (DOI)
- Is published in
- Other: 10.25504/FAIRsharing.0b5a1d (DOI)
References
- [1] Fedorov, A., Longabaugh, W. J. R., Pot, D., Clunie, D. A., Pieper, S. D., Gibbs, D. L., Bridge, C., Herrmann, M. D., Homeyer, A., Lewis, R., Aerts, H. J. W. L., Krishnaswamy, D., Thiriveedhi, V. K., Ciausu, C., Schacherer, D. P., Bontempi, D., Pihl, T., Wagner, U., Farahani, K., Kim, E. & Kikinis, R. National cancer institute imaging data commons: Toward transparency, reproducibility, and scalability in imaging artificial intelligence. Radiographics 43, (2023).
- [2] Roth, H., Farag, A., Turkbey, E. B., Lu, L., Liu, J., & Summers, R. M. (2016). Data From Pancreas-CT (Version 2) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2016.tNB1kqBU
- [3] 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.
- [4] Herz, C., Fillion-Robin, J.-C., Onken, M., Riesmeier, J., Lasso, A., Pinter, C., Fichtinger, G., Pieper, S., Clunie, D., Kikinis, R. & Fedorov, A. dcmqi: An Open Source Library for Standardized Communication of Quantitative Image Analysis Results Using DICOM. Cancer Res. 77, e87–e90 (2017).