Exploring 3D U-Net Training Configurations and Post-Processing Strategies for the MICCAI 2023 Kidney and Tumor Segmentation Challenge
This work addresses the problem of inter-observer variability in kidney cancer diagnosis for medical imaging, but it is incremental as it builds on existing 3D U-Net methods.
The paper tackled accurate segmentation of kidneys, cysts, and kidney tumors in CT images by exploring 3D U-Net training configurations and post-processing strategies, achieving second place in the KiTS23 challenge with an average Dice score of 0.820 and Surface Dice of 0.712.
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.