Quantitative organ assessment is an essential step in automated abdominal
disease diagnosis and treatment planning. Artificial intelligence (AI) has
shown great potential to automatize this process. However, most existing AI
algorithms rely on many expert annotations and lack a comprehensive evaluation
of accuracy and efficiency in real-world multinational settings. To overcome
these limitations, we organized the FLARE 2022 Challenge, the largest abdominal
organ analysis challenge to date, to benchmark fast, low-resource, accurate,
annotation-efficient, and generalized AI algorithms. We constructed an
intercontinental and multinational dataset from more than 50 medical groups,
including Computed Tomography (CT) scans with different races, diseases,
phases, and manufacturers. We independently validated that a set of AI
algorithms achieved a median Dice Similarity Coefficient (DSC) of 90.0\% by
using 50 labeled scans and 2000 unlabeled scans, which can significantly reduce
annotation requirements. The best-performing algorithms successfully
generalized to holdout external validation sets, achieving a median DSC of
89.5\%, 90.9\%, and 88.3\% on North American, European, and Asian cohorts,
respectively. They also enabled automatic extraction of key organ biology
features, which was labor-intensive with traditional manual measurements. This
opens the potential to use unlabeled data to boost performance and alleviate
annotation shortages for modern AI models.

定量器官评估是自动腹部疾病诊断和治疗规划的重要步骤。人工智能（AI）在自动化这一过程中显示出巨大的潜力。为了克服现有 AI 算法在现实世界多国家设置中对准确性和效率的大规模评估以及缺乏全面评估的限制，我们组织了迄今最大规模的腹部器官分析挑战 FLARE 2022 Challenge，旨在评估快速、低资源、准确、注释高效和泛化能力强的 AI 算法。我们从 50 多个医疗团体构建了一个跨洲际和跨国家的数据集，包括来自不同种族、疾病、阶段和制造商的计算机断层扫描（CT 扫描）。我们独立验证了一组 AI 算法通过使用 50 个标记扫描和 2000 个未标记扫描实现了 90.0% 的中位数 Dice 相似系数（DSC），这可以显著减少标注要求。最佳表现的算法成功泛化到保留的外部验证集，分别在北美、欧洲和亚洲队列上实现了中位数 DSC 分别为 89.5%、90.9% 和 88.3%。它们还实现了关键器官生物学特征的自动提取，这在传统的手动测量中需要耗费大量人力。这有望利用未标记数据提升性能并缓解现代 AI 模型的标注短缺问题。