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.

本研究解决了现有腹部CT扫描中器官和癌症分割方法局限于特定癌症类型的问题。通过举办国际竞赛，提供了一个大规模、多样化的数据集，从40多个医疗中心收集的4650个CT扫描图像，研究团队采用基于深度学习的级联框架，实现了器官和病灶的高准确性分割，显著推动了该领域的创新和发展。

腹部CT中的自动器官和泛癌症分割：FLARE 2023挑战