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.

在肿瘤患者的随访 CT 检查中，肿瘤体征的尺寸测量对于评估治疗效果至关重要。高效的病变分割能够加速放射学工作流程。我们引入了 ULS23 基准，用于胸腹盆 CT 检查中的 3D 通用病变分割。该基准包含了胰腺、结肠和骨骼等各种挑战性的病变类型，在 284 名患者中共计包含 38,693 个病变。我们还开发并公开发布了我们的基线半监督 3D 病变分割模型。该模型在挑战测试集上取得了平均 Dice 系数为 0.703±0.240 的成绩。我们邀请持续提交以推动未来 ULS 模型的发展。