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Intrathoracic airway segmentation in computed tomography (CT) is aprerequisite for various respiratory disease analyses such as chronicobstructive pulmonary disease (COPD), asthma and lung cancer. Unlike otherorgans with simpler shapes or topology, the airway's complex tree structureimposes an unbearable burden to generate the "ground truth" label (up to 7 or 3hours of manual or semi-automatic annotation on each case). Most of theexisting airway datasets are incompletely labeled/annotated, thus limiting thecompleteness of computer-segmented airway. In this paper, we propose a newanatomy-aware multi-class airway segmentation method enhanced bytopology-guided iterative self-learning. Based on the natural airway anatomy,we formulate a simple yet highly effective anatomy-aware multi-classsegmentation task to intuitively handle the severe intra-class imbalance of theairway. To solve the incomplete labeling issue, we propose a tailoredself-iterative learning scheme to segment toward the complete airway tree. Forgenerating pseudo-labels to achieve higher sensitivity , we introduce a novelbreakage attention map and design a topology-guided pseudo-label refinementmethod by iteratively connecting breaking branches commonly existed frominitial pseudo-labels. Extensive experiments have been conducted on fourdatasets including two public challenges. The proposed method ranked 1st inboth EXACT'09 challenge using average score and ATM'22 challenge on weightedaverage score. In a public BAS dataset and a private lung cancer dataset, ourmethod significantly improves previous leading approaches by extracting atleast (absolute) 7.5% more detected tree length and 4.0% more tree branches,while maintaining similar precision.

Accurate Airway Tree Segmentation in CT Scans via Anatomy-aware Multi-class Segmentation and Topology-guided Iterative Learning