Automated detection of tumour lesions on positron emission tomography–computed tomography (PET/CT) image data is a clinically relevant but highly challenging task. Progress in this field has been hampered in the past owing to the lack of publicly available annotated data and limited availability of platforms for inter-institutional collaboration. Here we describe the results of the autoPET challenge, a biomedical image analysis challenge aimed to motivate research in the field of automated PET/CT image analysis. The challenge task was the automated segmentation of metabolically active tumour lesions on whole-body ¹⁸F-fluorodeoxyglucose PET/CT. Challenge participants had access to a large publicly available annotated PET/CT dataset for algorithm training. All algorithms submitted to the final challenge phase were based on deep learning methods, mostly using three-dimensional U-Net architectures. Submitted algorithms were evaluated on a private test set composed of 150 PET/CT studies from two institutions. An ensemble model of the highest-ranking algorithms achieved favourable performance compared with individual algorithms. Algorithm performance was dependent on the quality and quantity of data and on algorithm design choices, such as tailored post-processing of predicted segmentations. Future iterations of this challenge will focus on generalization and clinical translation.

在正电子发射断层扫描-计算机断层扫描 （PET/CT） 图像数据上自动检测肿瘤病变是一项临床相关但极具挑战性的任务。由于缺乏公开可用的注释数据和机构间合作平台的可用性有限，该领域的进展在过去受到阻碍。在这里，我们描述了 autoPET 挑战赛的结果，这是一项生物医学图像分析挑战赛，旨在激励自动 PET/CT 图像分析领域的研究。挑战任务是在全身 ¹⁸F-氟脱氧葡萄糖 PET/CT 上自动分割代谢活跃的肿瘤病灶。挑战参与者可以访问一个大型公开可用的注释 PET/CT 数据集进行算法训练。提交到最终挑战阶段的所有算法都基于深度学习方法，主要使用三维 U-Net 架构。提交的算法在由来自两个机构的 150 项 PET/CT 研究组成的私人测试集上进行评估。与单个算法相比，最高排名算法的集成模型取得了良好的性能。算法性能取决于数据的质量和数量以及算法设计选择，例如对预测分割的定制后处理。这项挑战的未来迭代将侧重于泛化和临床转化。