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