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

TL;DR

The paper introduces the ULS23 benchmark dataset and baseline model for universal 3D lesion segmentation in CT scans, covering diverse lesion types across multiple organs to facilitate research and development in this area.

Contribution

It provides a large, diverse dataset and a baseline semi-supervised segmentation model for universal lesion segmentation in CT scans, addressing a gap in existing benchmarks.

Findings

Baseline model achieved Dice coefficient of 0.703 on test set.

ULS23 dataset includes 38,693 lesions across multiple organs.

Benchmark is publicly accessible for research use.

Abstract

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 $\pm$ 0.240 on the challenge test set. We invite ongoing submissions to advance the development of future ULS models.