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In 2023, it is estimated that 81,800 kidney cancer cases will be newlydiagnosed, and 14,890 people will die from this cancer in the United States.Preoperative dynamic contrast-enhanced abdominal computed tomography (CT) isoften used for detecting lesions. However, there exists inter-observervariability due to subtle differences in the imaging features of kidney andkidney tumors. In this paper, we explore various 3D U-Net trainingconfigurations and effective post-processing strategies for accuratesegmentation of kidneys, cysts, and kidney tumors in CT images. We validatedour model on the dataset of the 2023 Kidney and Kidney Tumor Segmentation(KiTS23) challenge. Our method took second place in the final ranking of theKiTS23 challenge on unseen test data with an average Dice score of 0.820 and anaverage Surface Dice of 0.712.

Exploring 3D U-Net Training Configurations and Post-Processing Strategies for the MICCAI 2023 Kidney and Tumor Segmentation Challenge