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.

翻译：胸部CT图像中的气道分割是慢性阻塞性肺疾病(COPD)、哮喘和肺癌等多种呼吸系统疾病分析的前提。与其他形状或拓扑结构更简单的器官不同，气道复杂的树状结构给生成"金标准"标签带来了难以承受的负担（每例手动或半自动标注需耗时7至3小时）。现有大多数气道数据集存在标注不完全的问题，从而限制了计算机分割气道的完整性。本文提出了一种新的基于解剖感知的多类气道分割方法，并通过拓扑引导的迭代自学习进行增强。基于气道的自然解剖结构，我们设计了一个简单而高效的解剖感知多类分割任务，以直观地处理气道严重的类内不平衡问题。为解决标注不完全的难题，我们提出了一种定制的自迭代学习方案，旨在分割出完整的气道树。为生成具有更高灵敏度的伪标签，我们引入了一种新颖的断裂注意力图，并设计了一种拓扑引导的伪标签优化方法，通过迭代连接初始伪标签中普遍存在的断裂分支。我们在四个数据集（包括两个公开挑战赛）上进行了广泛实验。所提方法在EXACT'09挑战赛（按平均分）和ATM'22挑战赛（按加权平均分）中均排名第一。在一个公开的BAS数据集和一个私有肺癌数据集上，我们的方法通过额外提取至少（绝对）7.5%的检测树长度和4.0%的树分支，显著改进了以往领先方法，同时保持了相似的精度。