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C4KC-KITS

C4KC-KiTS | Data from the training set of the 2019 Kidney and Kidney Tumor Segmentation Challenge

DOI: 10.7937/TCIA.2019.IX49E8NX | Data Citation Required | Image Collection

Location Species Subjects Data Types Cancer Types Size Status Updated
Kidney Human 210 CT, SEG Kidney Cancer 40.72GB Public, Complete 2020/06/18

Summary

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.

Data Access

Version 3: Updated 2020/06/18

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.

Title Data Type Format Access Points Subjects Studies Series Images License
Images and Segmentations SEG, CT DICOM
Download requires NBIA Data Retriever
210 210 621 71,423 CC BY 3.0
Clinical Data CSV 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.

  • Imaging Data Commons (IDC) (Imaging 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

    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

    Detailed Description

    Note:  

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

    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

    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

    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. 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 2: Updated 2020/03/23

    Added clinical data spreadsheet. 

    Title Data Type Format Access Points Studies Series Images License
    Images and Segmentations DICOM
    Clinical Data CSV

    Version 1: Updated 2019/12/18

    Title Data Type Format Access Points Studies Series Images License
    Images DICOM
    Analysis Results Using This Collection