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PEDIATRIC-CT-SEG

The Cancer Imaging Archive

Pediatric-CT-SEG | Pediatric Chest/Abdomen/Pelvic CT Exams with Expert Organ Contours

DOI: 10.7937/TCIA.X0H0-1706 | Data Citation Required | Image Collection

Location Species Subjects Data Types Cancer Types Size Status Updated
Various Human 359 RTSTRUCT, CT Non-Cancer 65.69GB Public, Complete 2022/03/31

Summary

This dataset was collected by a collaboration of researchers from Children’s Wisconsin, Marquette University, Varian Medical Systems, Medical College of Wisconsin, and Stanford University as part of a project funded by the National Institute of Biomedical Imaging and Bioengineering (U01EB023822) to develop tools for rapid, patient-specific CT organ dose estimation. The collection consists of CT images in DICOM format of 359 pediatric chest-abdomen-pelvis or abdomen-pelvis exams acquired from three CT scanners. The datasets represent random pediatric cases based upon routine clinical indications. Each dataset contains expert contours of up to twenty-nine structures in DICOM RTSS format.  Some datasets are missing structures that are not in the scan range or that, in younger patients, could not be reliably identified. Patient ages range from 5 days to 16 years, with a mean age of 7 and with a near equal distribution of male (180) and female (179) patients. The CT acquisition protocols and reconstruction methods vary across the scanner models and patient sizes, with specifications available in the DICOM headers. This data can be used to develop autosegmentation methods for radiation therapy, CT dosimetry, CT diagnostic algorithms, or other applications. The metadata of each CT image series contains the correct patient age and the height and weight data when available.

The native slice thickness for the acquired images was 0.625 mm for the GE scanners and 0.6 mm for the Siemens scanners.  Sixty-two datasets were manually contoured at this native slice thickness.  However, this process required extensive manual labor and was also challenged by high noise in the thin slices.  Therefore, the subsequent 297 datasets were reformatted to 2.0-mm slice thickness using a cubic spline interpolation algorithm prior to contouring. For some datasets, this interpolation caused artifacts in the most inferior or superior slices in the volume.  This is a known limitation of this dataset and users may need to disregard these corrupted slices, depending on the intended application. 

Data Access

Version 2: Updated

Note: Corrected RTSTRUCTs – 103 RTSTRUCT series incorrectly contained 2 files, where one file had skin contours with errors and one file had corrected skin contours. There is now only 1 file per each RTSTRUCT series containing the corrected skin contours. Also note, the new RTSTRUCT data directory path, when downloaded using the descriptive download option, will be slightly different from downloads of the previous version and will contain “NA” in the directory path while downloads of the older version will contain “RTSTRUCT” in the path. 

Title Data Type Format Access Points Subjects Studies Series Images License
Images and Radiation Therapy Structures RTSTRUCT, CT DICOM
Download requires NBIA Data Retriever
359 359 718 110,442 CC BY 4.0

Additional Resources for this Dataset

The NCI Cancer Research Data Commons (CRDC) provides access to additional data and a cloud-based data science infrastructure that connects data sets with analytics tools to allow users to share, integrate, analyze, and visualize cancer research data.

Citations & Data Usage Policy

Data Citation Required: Users must abide by the TCIA Data Usage Policy and Restrictions. Attribution must include the following citation, including the Digital Object Identifier:

Data Citation

Jordan, P., Adamson, P. M., Bhattbhatt, V., Beriwal, S., Shen, S., Radermecker, O., Bose, S., Strain, L. S., Offe, M., Fraley, D., Principi, S., Ye, D. H., Wang, A. S., Van Heteren, J., Vo, N.-J., & Schmidt, T. G. (2021). Pediatric Chest/Abdomen/Pelvic CT Exams with Expert Organ Contours (Pediatric-CT-SEG) (Version 2) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/TCIA.X0H0-1706

Acknowledgements

We would like to acknowledge the individuals and institutions that have provided data for this collection:

The authors would like to thank Kate Bruckner, Jessica Contreras, Sean Hergenrother, Maddie Johnson, Peter Lamberton, Liz McMahon and Jadie Rezach of Marquette University for the bone contouring efforts.  We would also like to thank dosimetrists Alyssa Olson and Dana Cole for the initial contour protocol evaluation and Dr. Gordan Wong for contour review.  The authors also thank Lara Dyke of Varian Medical Systems for the Eclipse support.  This work was funded in part by NIH U01EB023822.

Other Publications Using this Data

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

Adamson, P. M., Bhattbhatt, V., Principi, S., Beriwal, S., Strain, L. S., Offe, M., Wang, A. S., Vo, N., Gilat Schmidt, T., & Jordan, P. (2022). Technical note: Evaluation of a V‐Net autosegmentation algorithm for pediatric CT scans: Performance, generalizability, and application to patient‐specific CT dosimetry. In Medical Physics (Vol. 49, Issue 4, pp. 2342–2354). Wiley. https://doi.org/10.1002/mp.15521

Publication Citation

Jordan, P., Adamson, P. M., Bhattbhatt, V., Beriwal, S., Shen, S., Radermecker, O., Bose, S., Strain, L. S., Offe, M., Fraley, D., Principi, S., Ye, D. H., Wang, A. S., Heteren, J., Vo, N., & Schmidt, T. G. (2022). Pediatric chest‐abdomen‐pelvis and abdomen‐pelvis CT images with expert organ contours. In Medical Physics (Vol. 49, Issue 5, pp. 3523–3528). Wiley. https://doi.org/10.1002/mp.15485

TCIA 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. In Journal of Digital Imaging (Vol. 26, Issue 6, pp. 1045–1057). Springer Science and Business Media LLC. https://doi.org/10.1007/s10278-013-9622-7

Previous Versions

Version 1: Updated 2021/11/30

Title Data Type Format Access Points Studies Series Images License
Images DICOM
Download requires NBIA Data Retriever