Open international challenges are becoming the de facto standard for assessing computer vision and image analysis algorithms. In recent years, new methods have extended the reach of pulmonary airway segmentation that is closer to the limit of image resolution. Since EXACT'09 pulmonary airway segmentation, limited effort has been directed to quantitative comparison of newly emerged algorithms driven by the maturity of deep learning based approaches and clinical drive for resolving finer details of distal airways for early intervention of pulmonary diseases. Thus far, public annotated datasets are extremely limited, hindering the development of data-driven methods and detailed performance evaluation of new algorithms. To provide a benchmark for the medical imaging community, we organized the Multi-site, Multi-domain Airway Tree Modeling (ATM'22), which was held as an official challenge event during the MICCAI 2022 conference. ATM'22 provides large-scale CT scans with detailed pulmonary airway annotation, including 500 CT scans (300 for training, 50 for validation, and 150 for testing). The dataset was collected from different sites and it further included a portion of noisy COVID-19 CTs with ground-glass opacity and consolidation. Twenty-three teams participated in the entire phase of the challenge and the algorithms for the top ten teams are reviewed in this paper. Quantitative and qualitative results revealed that deep learning models embedded with the topological continuity enhancement achieved superior performance in general. ATM'22 challenge holds as an open-call design, the training data and the gold standard evaluation are available upon successful registration via its homepage.

翻译：开放国际挑战赛正逐渐成为评估计算机视觉和图像分析算法的事实标准。近年来，新方法将肺气道分割的边界延伸至更接近图像分辨率极限。自EXACT'09肺气道分割竞赛以来，受深度学习方法的成熟及临床驱动对远端气道更精细细节（以早期干预肺部疾病）的需求，针对新涌现算法的定量比较研究投入有限。目前，公开标注数据集极度匮乏，阻碍了数据驱动方法的发展及新算法的详细性能评估。为医学影像社区提供基准，我们组织了多站点、多领域气道树建模（ATM'22），其作为MICCAI 2022会议期间官方挑战赛举办。ATM'22提供含详细肺气道标注的大规模CT扫描数据，共计500例CT扫描（训练集300例、验证集50例、测试集150例）。数据集采集自不同站点，并包含部分伴有磨玻璃影和实变的噪声COVID-19 CT扫描。二十三支团队参与了挑战赛全程，本文对前十名团队的算法进行了综述。定量与定性结果表明，嵌入拓扑连续性增强的深度学习模型普遍表现更优。ATM'22挑战赛采用开放征集设计，训练数据及黄金标准评估结果在成功注册其官网后可获取。