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

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arxiv 2312.05528 v1 pith:M65DKYR7 submitted 2023-12-09 eess.IV cs.CV

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

Kwang-Hyun Uhm , Hyunjun Cho , Zhixin Xu , Seohoon Lim , Seung-Won Jung , Sung-Hoo Hong , Sung-Jea Ko This is my paper
classification eess.IV cs.CV
keywords kidneychallengesegmentationaveragecancerconfigurationsdicekits23
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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.

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Cited by 2 Pith papers

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  2. Submanifold Sparse Convolutional Networks for Automated 3D Segmentation of Kidneys and Kidney Tumours in Computed Tomography

    cs.CV 2025-11 conditional novelty 5.0

    A two-stage sparse convolutional network pipeline for native high-resolution 3D kidney and tumor segmentation in CT that matches top Dice scores while reducing VRAM and runtime versus nnU-Net and SegVol.