# Robust Renal Mass Segmentation on CT: A Validation Study of an AI-Based Framework

arXiv: https://arxiv.org/abs/2505.07573 · HTML: https://mlsanity.com/paper/2505.07573/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 47 references that are in the map.
- First posted on arXiv 12 May 2025.

## Builds on (most cited first, 14 of 14)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [TotalSegmentator: robust segmentation of 104 anatomical structures in CT images](https://mlsanity.com/paper/2208.05868/) (2022)
- [nnU-Net Revisited: A Call for Rigorous Validation in 3D Medical Image Segmentation](https://mlsanity.com/paper/2404.09556/) (2024)
- [Metrics reloaded: Recommendations for image analysis validation](https://mlsanity.com/paper/2206.01653/) (2022)
- [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 KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CT](https://mlsanity.com/paper/2307.01984/) (2023)
- [The ULS23 Challenge: a Baseline Model and Benchmark Dataset for 3D Universal Lesion Segmentation in Computed Tomography](https://mlsanity.com/paper/2406.05231/) (2024)
- [Automated 3D Segmentation of Kidneys and Tumors in MICCAI KiTS 2023 Challenge](https://mlsanity.com/paper/2310.04110/) (2023)
- [Exploring 3D U-Net Training Configurations and Post-Processing Strategies for the MICCAI 2023 Kidney and Tumor Segmentation Challenge](https://mlsanity.com/paper/2312.05528/) (2023)
- [AI-Generated Annotations Dataset for Diverse Cancer Radiology Collections in NCI Image Data Commons](https://mlsanity.com/paper/2310.14897/) (2023)
- [Automatic Renal Segmentation in DCE-MRI using Convolutional Neural Networks](https://mlsanity.com/paper/1712.07022/) (2017)
- [Kidney abnormality segmentation in thorax-abdomen CT scans](https://mlsanity.com/paper/2309.03383/) (2023)
- [Improve Cross-Modality Segmentation by Treating T1-Weighted MRI Images as Inverted CT Scans](https://mlsanity.com/paper/2405.03713/) (2024)
- [CTARR: A fast and robust method for identifying anatomical regions on CT images via atlas registration](https://mlsanity.com/paper/2410.02316/) (2024)

## Built on by (most cited first, 3 of 3)

- [Sex-based Bias Inherent in the Dice Similarity Coefficient: A Model Independent Analysis for Multiple Anatomical Structures](https://mlsanity.com/paper/2509.19778/) (2025)
- [Kidney Cancer Detection Using 3D-Based Latent Diffusion Models](https://mlsanity.com/paper/2601.05852/) (2026)
- [Benchmarking Foundation Models for Renal Lesion Stratification in CT](https://mlsanity.com/paper/2605.07749/) (2026)

## Abstract (arXiv)

Renal mass segmentation has important potential to enhance the clinical workflow, especially in settings requiring quantitative assessments. Kidney volume could serve as an important biomarker for renal diseases, with changes in volume correlating directly with kidney function. Currently, clinical practice often relies on subjective visual assessment for evaluating kidney size and kidney lesions, including tumors and cysts, which are typically staged based on diameter, volume, and anatomical location. To support a more objective and reproducible approach, this research aims to develop a robust, thoroughly validated renal mass segmentation algorithm, named Renal-Net. We employ publicly available training datasets and leverage the state-of-the-art medical image segmentation framework nnU-Net. Validation is conducted using both proprietary and public test datasets, with segmentation performance quantified by Dice coefficient and the 95th percentile Hausdorff distance. Furthermore, we analyze robustness across subgroups based on patient sex, age, CT contrast phases, and tumor histologic subtypes. Our findings demonstrate that our segmentation algorithm, trained exclusively on publicly available data, generalizes effectively to external test sets and outperforms existing state-of-the-art models across all tested datasets. Subgroup analyses reveal consistent high performance, indicating strong robustness and reliability. The developed algorithm and associated code are publicly accessible at https://github.com/DIAGNijmegen/oncology-kidney-abnormality-segmentation.