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 taskThis collection contains 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
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 WeightWe 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.
| Localtab Group | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|