  Intrathoracic airway segmentation in computed tomography (CT) is a
prerequisite for various respiratory disease analyses such as chronic
obstructive pulmonary disease (COPD), asthma and lung cancer. Unlike other
organs with simpler shapes or topology, the airway's complex tree structure
imposes an unbearable burden to generate the "ground truth" label (up to 7 or 3
hours of manual or semi-automatic annotation on each case). Most of the
existing airway datasets are incompletely labeled/annotated, thus limiting the
completeness of computer-segmented airway. In this paper, we propose a new
anatomy-aware multi-class airway segmentation method enhanced by
topology-guided iterative self-learning. Based on the natural airway anatomy,
we formulate a simple yet highly effective anatomy-aware multi-class
segmentation task to intuitively handle the severe intra-class imbalance of the
airway. To solve the incomplete labeling issue, we propose a tailored
self-iterative learning scheme to segment toward the complete airway tree. For
generating pseudo-labels to achieve higher sensitivity , we introduce a novel
breakage attention map and design a topology-guided pseudo-label refinement
method by iteratively connecting breaking branches commonly existed from
initial pseudo-labels. Extensive experiments have been conducted on four
datasets including two public challenges. The proposed method ranked 1st in
both EXACT'09 challenge using average score and ATM'22 challenge on weighted
average score. In a public BAS dataset and a private lung cancer dataset, our
method significantly improves previous leading approaches by extracting at
least (absolute) 7.5% more detected tree length and 4.0% more tree branches,
while maintaining similar precision.
