Abstract
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.