  There is a large body of literature linking anatomic and geometric
characteristics of kidney tumors to perioperative and oncologic outcomes.
Semantic segmentation of these tumors and their host kidneys is a promising
tool for quantitatively characterizing these lesions, but its adoption is
limited due to the manual effort required to produce high-quality 3D
segmentations of these structures. Recently, methods based on deep learning
have shown excellent results in automatic 3D segmentation, but they require
large datasets for training, and there remains little consensus on which
methods perform best. The 2019 Kidney and Kidney Tumor Segmentation challenge
(KiTS19) was a competition held in conjunction with the 2019 International
Conference on Medical Image Computing and Computer Assisted Intervention
(MICCAI) which sought to address these issues and stimulate progress on this
automatic segmentation problem. A training set of 210 cross sectional CT images
with kidney tumors was publicly released with corresponding semantic
segmentation masks. 106 teams from five continents used this data to develop
automated systems to predict the true segmentation masks on a test set of 90 CT
images for which the corresponding ground truth segmentations were kept
private. These predictions were scored and ranked according to their average So
rensen-Dice coefficient between the kidney and tumor across all 90 cases. The
winning team achieved a Dice of 0.974 for kidney and 0.851 for tumor,
approaching the inter-annotator performance on kidney (0.983) but falling short
on tumor (0.923). This challenge has now entered an "open leaderboard" phase
where it serves as a challenging benchmark in 3D semantic segmentation.
