# The ULS23 Challenge: a Baseline Model and Benchmark Dataset for 3D Universal Lesion Segmentation in Computed Tomography

arXiv: https://arxiv.org/abs/2406.05231 · HTML: https://mlsanity.com/paper/2406.05231/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 45 references that are in the map.
- First posted on arXiv 7 Jun 2024.

## Builds on (most cited first, 17 of 17)

- [The Medical Segmentation Decathlon](https://mlsanity.com/paper/2106.05735/) (2021)
- [The Liver Tumor Segmentation Benchmark (LiTS)](https://mlsanity.com/paper/1901.04056/) (2019)
- [Segment Anything Model for Medical Image Analysis: an Experimental Study](https://mlsanity.com/paper/2304.10517/) (2023)
- [nnU-Net Revisited: A Call for Rigorous Validation in 3D Medical Image Segmentation](https://mlsanity.com/paper/2404.09556/) (2024)
- [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)
- [A New 2.5D Representation for Lymph Node Detection using Random Sets of Deep Convolutional Neural Network Observations](https://mlsanity.com/paper/1406.2639/) (2014)
- [Deep Lesion Graphs in the Wild: Relationship Learning and Organization of Significant Radiology Image Findings in a Diverse Large-scale Lesion Database](https://mlsanity.com/paper/1711.10535/) (2017)
- [MULAN: Multitask Universal Lesion Analysis Network for Joint Lesion Detection, Tagging, and Segmentation](https://mlsanity.com/paper/1908.04373/) (2019)
- [Automated Design of Deep Learning Methods for Biomedical Image Segmentation](https://mlsanity.com/paper/1904.08128/) (2019)
- [Deep Lesion Tracker: Monitoring Lesions in 4D Longitudinal Imaging Studies](https://mlsanity.com/paper/2012.04872/) (2020)
- [Accurate Weakly-Supervised Deep Lesion Segmentation using Large-Scale Clinical Annotations: Slice-Propagated 3D Mask Generation from 2D RECIST](https://mlsanity.com/paper/1807.01172/) (2018)
- [Weakly-Supervised Universal Lesion Segmentation with Regional Level Set Loss](https://mlsanity.com/paper/2105.01218/) (2021)
- [One Click Lesion RECIST Measurement and Segmentation on CT Scans](https://mlsanity.com/paper/2007.11087/) (2020)
- [Lesion Segmentation and RECIST Diameter Prediction via Click-driven Attention and Dual-path Connection](https://mlsanity.com/paper/2105.01828/) (2021)
- [Weakly Supervised Lesion Co-segmentation on CT Scans](https://mlsanity.com/paper/2001.09174/) (2020)
- [Accurate and Robust Lesion RECIST Diameter Prediction and Segmentation with Transformers](https://mlsanity.com/paper/2208.13113/) (2022)

## Built on by (most cited first, 26 of 26)

- [nnInteractive: Redefining 3D Promptable Segmentation](https://mlsanity.com/paper/2503.08373/) (2025)
- [ReXGroundingCT: A 3D Chest CT Dataset for Segmentation of Findings from Free-Text Reports](https://mlsanity.com/paper/2507.22030/) (2025)
- [Scaling Tumor Segmentation: Best Lessons from Real and Synthetic Data](https://mlsanity.com/paper/2510.14831/) (2025)
- [LesionLocator: Zero-Shot Universal Tumor Segmentation and Tracking in 3D Whole-Body Imaging](https://mlsanity.com/paper/2502.20985/) (2025)
- [Scaling Artificial Intelligence for Multi-Tumor Early Detection with More Reports, Fewer Masks](https://mlsanity.com/paper/2510.14803/) (2025)
- [MSWAL: 3D Multi-class Segmentation of Whole Abdominal Lesions Dataset](https://mlsanity.com/paper/2503.13560/) (2025)
- [ONCOPILOT: A Promptable CT Foundation Model For Solid Tumor Evaluation](https://mlsanity.com/paper/2410.07908/) (2024)
- [Conformal Lesion Segmentation for 3D Medical Images](https://mlsanity.com/paper/2510.17897/) (2025)
- [RadThinking: A Dataset for Longitudinal Clinical Reasoning in Radiology](https://mlsanity.com/paper/2605.10761/) (2026)
- [Robust Renal Mass Segmentation on CT: A Validation Study of an AI-Based Framework](https://mlsanity.com/paper/2505.07573/) (2025)
- [Designing UNICORN: a Unified Benchmark for Imaging in Computational Pathology, Radiology, and Natural Language](https://mlsanity.com/paper/2603.02790/) (2026)
- [Rethinking Whole-Body CT Image Interpretation: An Abnormality-Centric Approach](https://mlsanity.com/paper/2506.03238/) (2025)
- [Unstable Prompts, Unreliable Segmentations: A Challenge for Longitudinal Lesion Analysis](https://mlsanity.com/paper/2507.19230/) (2025)
- [Text Embedded Swin-UMamba for DeepLesion Segmentation](https://mlsanity.com/paper/2508.06453/) (2025)
- [State of Abdominal CT Datasets: A Critical Review of Bias, Clinical Relevance, and Real-world Applicability](https://mlsanity.com/paper/2508.13626/) (2025)
- [Glance and Focus Reinforcement for Pan-cancer Screening](https://mlsanity.com/paper/2601.19103/) (2026)
- [Opportunistic Promptable Segmentation: Leveraging Routine Radiological Annotations to Guide 3D CT Lesion Segmentation](https://mlsanity.com/paper/2602.00309/) (2026)
- [3DLAND: 3D Lesion Abdominal Anomaly Localization Dataset](https://mlsanity.com/paper/2602.12820/) (2026)
- [Modality-Projection Universal Model for Comprehensive Full-Body Medical Imaging Segmentation](https://mlsanity.com/paper/2412.19026/) (2024)
- [RAPS-3D: Efficient interactive segmentation for 3D radiological imaging](https://mlsanity.com/paper/2507.07730/) (2025)
- [ULS+: Data-driven Model Adaptation Enhances Lesion Segmentation](https://mlsanity.com/paper/2601.02988/) (2026)
- [Exploiting Longitudinal Context in Clinician-Verified Interactive Lesion Tracking](https://mlsanity.com/paper/2605.23118/) (2026)
- [Effect of User-Prompted Priors on Semi-Automated Cancer Lesion Segmentation in Whole-Body Computed Tomography](https://mlsanity.com/paper/2607.24210/) (2026)
- [CoM3eT: A foundation model for medical image analysis through federated, multidimensional context integration](https://mlsanity.com/paper/2608.16268/) (2026)
- [RT-Super: Learning Tumor Segmentation from Longitudinal Images and Reports](https://mlsanity.com/paper/2609.35637/) (2026)
- [Merlin Plus: A Large-Scale, Multi-Cancer, Image-Mask-Report Dataset](https://mlsanity.com/paper/2609.36436/) (2026)

## Abstract (arXiv)

Size measurements of tumor manifestations on follow-up CT examinations are crucial for evaluating treatment outcomes in cancer patients. Efficient lesion segmentation can speed up these radiological workflows. While numerous benchmarks and challenges address lesion segmentation in specific organs like the liver, kidneys, and lungs, the larger variety of lesion types encountered in clinical practice demands a more universal approach. To address this gap, we introduced the ULS23 benchmark for 3D universal lesion segmentation in chest-abdomen-pelvis CT examinations. The ULS23 training dataset contains 38,693 lesions across this region, including challenging pancreatic, colon and bone lesions. For evaluation purposes, we curated a dataset comprising 775 lesions from 284 patients. Each of these lesions was identified as a target lesion in a clinical context, ensuring diversity and clinical relevance within this dataset. The ULS23 benchmark is publicly accessible via uls23.grand-challenge.org, enabling researchers worldwide to assess the performance of their segmentation methods. Furthermore, we have developed and publicly released our baseline semi-supervised 3D lesion segmentation model. This model achieved an average Dice coefficient of 0.703 ± 0.240 on the challenge test set. We invite ongoing submissions to advance the development of future ULS models.