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There is a large body of literature linking anatomic and geometriccharacteristics of kidney tumors to perioperative and oncologic outcomes.Semantic segmentation of these tumors and their host kidneys is a promisingtool for quantitatively characterizing these lesions, but its adoption islimited due to the manual effort required to produce high-quality 3Dsegmentations of these structures. Recently, methods based on deep learninghave shown excellent results in automatic 3D segmentation, but they requirelarge datasets for training, and there remains little consensus on whichmethods perform best. The 2019 Kidney and Kidney Tumor Segmentation challenge(KiTS19) was a competition held in conjunction with the 2019 InternationalConference on Medical Image Computing and Computer Assisted Intervention(MICCAI) which sought to address these issues and stimulate progress on thisautomatic segmentation problem. A training set of 210 cross sectional CT imageswith kidney tumors was publicly released with corresponding semanticsegmentation masks. 106 teams from five continents used this data to developautomated systems to predict the true segmentation masks on a test set of 90 CTimages for which the corresponding ground truth segmentations were keptprivate. These predictions were scored and ranked according to their average Sorensen-Dice coefficient between the kidney and tumor across all 90 cases. Thewinning 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 shorton tumor (0.923). This challenge has now entered an "open leaderboard" phasewhere it serves as a challenging benchmark in 3D semantic segmentation.

The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 Challenge