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Open international challenges are becoming the de facto standard forassessing computer vision and image analysis algorithms. In recent years, newmethods have extended the reach of pulmonary airway segmentation that is closerto the limit of image resolution. Since EXACT'09 pulmonary airway segmentation,limited effort has been directed to quantitative comparison of newly emergedalgorithms driven by the maturity of deep learning based approaches andclinical drive for resolving finer details of distal airways for earlyintervention of pulmonary diseases. Thus far, public annotated datasets areextremely limited, hindering the development of data-driven methods anddetailed performance evaluation of new algorithms. To provide a benchmark forthe medical imaging community, we organized the Multi-site, Multi-domain AirwayTree Modeling (ATM'22), which was held as an official challenge event duringthe MICCAI 2022 conference. ATM'22 provides large-scale CT scans with detailedpulmonary airway annotation, including 500 CT scans (300 for training, 50 forvalidation, and 150 for testing). The dataset was collected from differentsites and it further included a portion of noisy COVID-19 CTs with ground-glassopacity and consolidation. Twenty-three teams participated in the entire phaseof the challenge and the algorithms for the top ten teams are reviewed in thispaper. Quantitative and qualitative results revealed that deep learning modelsembedded with the topological continuity enhancement achieved superiorperformance in general. ATM'22 challenge holds as an open-call design, thetraining data and the gold standard evaluation are available upon successfulregistration via its homepage.

Multi-site, Multi-domain Airway Tree Modeling (ATM'22): A Public Benchmark for Pulmonary Airway Segmentation