Accurate airway segmentation from chest computed tomography (CT) scans is essential for quantitative lung analysis, yet manual annotation is impractical and many automated U-Net-based methods yield disconnected components that hinder reliable biomarker extraction. We present RepAir, a three-stage framework for robust 3D airway segmentation that combines an nnU-Net-based network with anatomically informed topology correction. The segmentation network produces an initial airway mask, after which a skeleton-based algorithm identifies potential discontinuities and proposes reconnections. A 1D convolutional classifier then determines which candidate links correspond to true anatomical branches versus false or obstructed paths. We evaluate RepAir on two distinct datasets: ATM'22, comprising annotated CT scans from predominantly healthy subjects and AeroPath, encompassing annotated scans with severe airway pathology. Across both datasets, RepAir outperforms existing 3D U-Net-based approaches such as Bronchinet and NaviAirway on both voxel-level and topological metrics, and produces more complete and anatomically consistent airway trees while maintaining high segmentation accuracy.

翻译：从胸部计算机断层扫描（CT）图像中准确分割气道对于定量肺部分析至关重要，然而手动标注不切实际，且许多基于U-Net的自动化方法会产生不连通的分割结果，阻碍了可靠生物标志物的提取。我们提出RepAir，一个用于鲁棒三维气道分割的三阶段框架，该框架结合了基于nnU-Net的网络与基于解剖学知识的拓扑校正。分割网络首先生成初始气道掩码，随后基于骨架的算法识别潜在的不连续性并建议重新连接。接着，一维卷积分类器判定哪些候选连接对应于真实的解剖分支，而非虚假或阻塞的路径。我们在两个不同的数据集上评估RepAir：ATM'22，包含主要来自健康受试者的标注CT扫描；以及AeroPath，包含具有严重气道病理的标注扫描。在两个数据集上，RepAir在体素级别和拓扑指标上均优于现有的基于三维U-Net的方法，如Bronchinet和NaviAirway，并能在保持高分割精度的同时，生成更完整且解剖学一致的气道树结构。