  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.
