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  • Data from the training set of the 2019 Kidney and Kidney Tumor Segmentation Challenge (C4KC-KiTS)

Summary

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This collection contains CT scans and segmentations from subjects from the training set of the 2019 Kidney and Kidney Tumor Segmentation Challenge (KiTS19). The challenge aimed to accelerate progress in automatic 3D semantic segmentation by releasing a dataset of CT scans for 210 patients with manual semantic segmentations of the kidneys and tumors in the corticomedullary phase.

The imaging was collected during routine care of patients who were treated by either partial or radical nephrectomy at the University of Minnesota Medical Center. Many of the CT scans were acquired at referring institutions and are therefore heterogeneous in terms of scanner manufacturers and acquisition protocols. Semantic segmentations were performed by students under the supervision of an experienced urologic cancer surgeon.

Protocol

Please refer to the data descriptor manuscript for a comprehensive account of the data collection and annotation process - arXiv:1904.00445. The Clinical Trial Time Point is calculated from Day of Surgery.

Acknowledgements

We would like to acknowledge the following institutions for their support of the data collection and associated challenge:

  • Climb 4 Kidney Cancer - Many of the students who worked on the chart review and manual segmentations for this dataset were graciously supported by Climb 4 Kidney Cancer (C4KC) as "C4KC Scholars"
  • Intuitive Surgical - A prize of $5,000 was awarded by Intuitive Surgical to the KiTS19 Challenge's highest scoring team
  • The National Cancer Institute of The National Institutes of Health - This work was supported under Award Number R01CA225435. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health

  • Harmonization of the components of this dataset, including into standard DICOM representation, was supported in part by the NCI Imaging Data Commons consortium. NCI Imaging Data Commons consortium is supported by the contract number 19X037Q from Leidos Biomedical Research under Task Order HHSN26100071 from NCI

Data Access

Data TypeDownload all or Query/FilterLicense
Images and Segmentations (DICOM, 40.7GB)


   

(Download requires the NBIA Data Retriever)

CC BY 3.0

Clinical Data  (CSV, 82 kB)

 

CC BY 3.0


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.

Detailed Description

Collection Statistics

Radiology Image Statistics

Modalities

CT, SEG

Number of Participants

210

Number of Studies

210

Number of Series

621

Number of Images

71,423

Image Size (GB)40.7

Note:  

The segmentation corresponds to the arterial phase in every case. No processing or analysis was done on the other phases.

Citations & Data Usage Policy 

Users must abide by the TCIA Data Usage Policy and Restrictions. Attribution should include references to the following citations:

Data Citation

Heller, N., Sathianathen, N., Kalapara, A., Walczak, E., Moore, K., Kaluzniak, H., Rosenberg, J., Blake, P., Rengel, Z., Oestreich, M., Dean, J., Tradewell, M., Shah, A., Tejpaul, R., Edgerton, Z., Peterson, M., Raza, S., Regmi, S., Papanikolopoulos, N., Weight, C.  (2019) Data from C4KC-KiTS  [Data set]. The Cancer Imaging Archive. DOI: 10.7937/TCIA.2019.IX49E8NX

Publication Citation

Heller, N., Isensee, F., Maier-Hein, K. H., Hou, X., Xie, C., Li, F., Nan, Y., Mu, G., Lin, Z., Han, M., Yao, G., Gao, Y., Zhang, Y., Wang, Y., Hou, F., Yang, J., Xiong, G., Tian, J., Zhong, C.,  Ma, J., Rickman, J., Dean, J., Stai, B., Tejpaul, R., Oestreich, M., Blake, P., Kaluzniak, H., Raza, S., Rosenberg, J., Moore, K., Walczak, E., Rengel, Z., Edgerton, Z., Vasdev, R., Peterson, M., McSweeney, S., Peterson, S., Kalapara, A., Sathianathen, N., Papanikolopoulos, N., Weight, C. (2021). The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 challenge. Medical Image Analysis, 67, 101821. https://doi.org/10.1016/j.media.2020.101821

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, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057. DOI: 10.1007/s10278-013-9622-7

Other Publications Using This Data

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

Version 3 (Current): Updated 2020/06/18

Data TypeDownload all or Query/Filter

Images and Segmentations 

(DICOM, 40.7GB)

   

(Requires NBIA Data Retriever)

Clinical Data  (CSV, 82 kB)

 

Upon initial publication of this dataset the segmentations were stored as sagittal series, while the CT images are axial. This caused difficulties loading this dataset into various DICOM tools. Those segmentations have now been converted (in a lossless fashion) to axial to resolve these issues.


Version 2: Updated 2020/03/23

Data TypeDownload all or Query/Filter

Images and Segmentations (DICOM, 40.7GB)

     Unavailable, see version 3 note.

Clinical Data  (CSV, 82 kB)

 

Added clinical data spreadsheet. 

Version 1: Updated 2019/12/18

Data TypeDownload all or Query/Filter

Images (DICOM, 40.7GB)

  Unavailable, see version 3 note.


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