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

arXiv: https://arxiv.org/abs/2312.05528 · HTML: https://mlsanity.com/paper/2312.05528/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 6 references that are in the map.
- First posted on arXiv 9 Dec 2023.

## Builds on (most cited first, 4 of 4)

- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation](https://mlsanity.com/paper/1809.10486/) (2018)
- [The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 Challenge](https://mlsanity.com/paper/1912.01054/) (2019)
- [The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical Outcomes](https://mlsanity.com/paper/1904.00445/) (2019)

## Built on by (most cited first, 6 of 6)

- [Lesion-Aware Cross-Phase Attention Network for Renal Tumor Subtype Classification on Multi-Phase CT Scans](https://mlsanity.com/paper/2406.16322/) (2024)
- [Robust Renal Mass Segmentation on CT: A Validation Study of an AI-Based Framework](https://mlsanity.com/paper/2505.07573/) (2025)
- [Submanifold Sparse Convolutional Networks for Automated 3D Segmentation of Kidneys and Kidney Tumours in Computed Tomography](https://mlsanity.com/paper/2511.04334/) (2025)
- [SEMIR: Semantic Minor-Induced Representation Learning on Graphs for Visual Segmentation](https://mlsanity.com/paper/2605.12389/) (2026)
- [Bridging the Applicator Gap with Data-Doping:Dual-Domain Learning for Precise Bladder Segmentation in CT-Guided Brachytherapy](https://mlsanity.com/paper/2601.20302/) (2026)
- [Lesion Segmentation in Moderate to Severe Traumatic Brain Injury: An nnU-Net Based Approach with Adaptive Normalization in the AIMS-TBI 2025 Challenge](https://mlsanity.com/paper/2607.12684/) (2026)

## Abstract (arXiv)

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.