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：开放式国际挑战赛正成为评估计算机视觉与图像分析算法的事实标准。近年来，新方法已将肺气道分割的覆盖范围扩展至更接近图像分辨率的极限。自EXACT'09肺气道分割挑战赛以来，由于基于深度学习的方法日趋成熟，以及临床需求推动对远端气道精细结构进行早期肺部疾病干预，针对新兴算法的定量比较研究投入有限。迄今为止，公开标注数据集极为匮乏，阻碍了数据驱动方法的发展及新算法的详细性能评估。为给医学影像学界提供基准，我们组织举办了多站点、多领域气道树建模挑战赛（ATM'22），该赛事作为官方挑战活动在MICCAI 2022会议期间举行。ATM'22提供了包含详细肺气道标注的大规模CT扫描数据，共计500例CT扫描（其中300例用于训练，50例用于验证，150例用于测试）。该数据集收集自不同站点，并进一步包含部分伴有磨玻璃影和实变的高噪声COVID-19 CT影像。23支团队全程参与了此次挑战赛，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取得了更优性能。ATM'22挑战赛采用开放式报名设计，训练数据与金标准评估工具可通过其主页成功注册后获取。