  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
relationship between kidney tumor morphology and clinical outcomes is difficult
due to data scarcity and the laborious nature of manually quantifying imaging
predictors. Automatic semantic segmentation of kidneys and kidney tumors is a
promising 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
and have been released publicly. With the presence of clinical context and
surgical 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 and semantic segmentation masks.
