Summary
The morphometry of a kidney tumor revealed by contrast-enhanced Computed Tomography (CT) imaging is an important factor in clinical decision making surrounding the lesion's diagnosis and treatment. Quantitative study of the association between kidney tumor morphometry and clinical outcomes is difficult due to data scarcity and the laborious nature of manually quantifying imaging predictors. Reliable semantic segmentation of kidneys and kidney tumors is a powerful tool towards automatically quantifying a wide array of morphometric features, but no sizeable annotated dataset is currently available to train models for this task.
Protocol
We present the KiTS19 challenge dataset: A collection of multi-phase CT imaging, segmentation masks, and comprehensive clinical outcomes for 300 patients who underwent nephrectomy for kidney tumors at our center between 2010 and 2018. 210 (70%) of these patients were selected at random as the training set for the 2019 MICCAI KiTS Kidney Tumor Segmentation Challenge. With the presence of comorbidities and clinical outcomes, this data can serve not only for benchmarking semantic segmentation models, but also for developing and studying biomarkers which make use of the imaging in conjunction with semantic segmentation masks.
Acknowledgements
This data set was provided to TCIA by Nicholas Heller, Niranjan Sathianathen, Arveen Kalapara, Edward Walczak, Keenan Moore, Heather Kaluzniak, Joel Rosenberg, Paul Blake, Zachary Rengel, Makinna Oestreich, Joshua Dean, Michael Tradewell, Aneri Shah, Resha Tejpaul, Zachary Edgerton, Matthew Peterson, Shaneabbas Raza, Subodh Regmi, Nikolaos Papanikolopoulos, and Christopher Weight
Data Access
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| Data Type | Download all or Query/Filter |
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| Images (DICOM, 13.4GB) | |
Acquisition & reconstruction settings (note: Acquisition and reconstruction settings within the DICOM header may be incorrect. The settings used for each scan are provided in the attached spreadsheet. |
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Detailed Description
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Modalities | RT STRUCT, CT |
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Citations & Data Usage Policy
This collection is freely available to browse, download, and use for commercial, scientific and educational purposes as outlined in the Creative Commons Attribution 3.0 Unported License. See TCIA's Data Usage Policies and Restrictions for additional details. Questions may be directed to help@cancerimagingarchive.net.
Please be sure to include the following citations in your work if you use this data set:
Manuscript Citation
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. 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
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Version 1 (Current): Updated
| Data Type | Download all or Query/Filter |
|---|---|
| Images (DICOM, 13.4 GB) |
(Requires NBIA Data Retriever.) |
| Acquisition & reconstruction settings (XLSX, 29kB) |