Organ and cancer segmentation in abdomen Computed Tomography (CT) scans is the prerequisite for precise cancer diagnosis and treatment. Most existing benchmarks and algorithms are tailored to specific cancer types, limiting their ability to provide comprehensive cancer analysis. This work presents the first international competition on abdominal organ and pan-cancer segmentation by providing a large-scale and diverse dataset, including 4650 CT scans with various cancer types from over 40 medical centers. The winning team established a new state-of-the-art with a deep learning-based cascaded framework, achieving average Dice Similarity Coefficient scores of 92.3% for organs and 64.9% for lesions on the hidden multi-national testing set. The dataset and code of top teams are publicly available, offering a benchmark platform to drive further innovations https://codalab.lisn.upsaclay.fr/competitions/12239.

翻译：腹部计算机断层扫描（CT）中的器官与癌症分割是实现精准癌症诊断与治疗的前提。现有大多数基准数据集与算法均针对特定癌症类型设计，限制了其提供全面癌症分析的能力。本研究通过提供一个大规模、多样化的数据集，举办了首个腹部器官与泛癌分割国际竞赛。该数据集包含来自40余家医疗中心的4650例涵盖多种癌症类型的CT扫描。获胜团队采用基于深度学习的级联框架，在隐藏的多国测试集上实现了器官平均Dice相似系数92.3%与病灶平均Dice相似系数64.9%的最新最优性能。数据集与优胜团队代码均已公开，为推动进一步创新提供了基准平台 https://codalab.lisn.upsaclay.fr/competitions/12239。