C4KC KiTS Challenge Kidney Tumor Segmentation Dataset

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. 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.