Accurate and complete segmentation of airways in chest CT images is essential for the quantitative assessment of lung diseases and the facilitation of pulmonary interventional procedures. Although deep learning has led to significant advancements in medical image segmentation, maintaining airway continuity remains particularly challenging. This difficulty arises primarily from the small and dispersed nature of airway structures, as well as class imbalance in CT scans. To address these challenges, we designed a Multi-scale Nested Residual U-Net (MNR-UNet), incorporating multi-scale inputs and Residual Multi-scale Modules (RMM) into a nested residual framework to enhance information flow, effectively capturing the intricate details of small airways and mitigating gradient vanishing. Building on this, we developed a three-stage segmentation pipeline to optimize the training of the MNR-UNet. The first two stages prioritize high accuracy and sensitivity, while the third stage focuses on repairing airway breakages to balance topological completeness and correctness. To further address class imbalance, we introduced a weighted Breakage-Aware Loss (wBAL) to heighten focus on challenging samples, penalizing breakages and thereby extending the length of the airway tree. Additionally, we proposed a hierarchical evaluation framework to offer more clinically meaningful analysis. Validation on both in-house and public datasets demonstrates that our approach achieves superior performance in detecting more accurate airway voxels and identifying additional branches, significantly improving airway topological completeness. The code will be released publicly following the publication of the paper.

翻译：胸部CT图像中气道的准确完整分割对于肺部疾病的定量评估和促进肺介入手术至关重要。尽管深度学习已显著推动医学图像分割的进展，但保持气道连续性仍面临特殊挑战。这一困难主要源于气道结构细小分散的特性以及CT扫描中的类别不平衡问题。为解决这些挑战，我们设计了一种多尺度嵌套残差U-Net（MNR-UNet），将多尺度输入和残差多尺度模块（RMM）整合到嵌套残差框架中，以增强信息流，有效捕捉细小气道的复杂细节并缓解梯度消失。在此基础上，我们开发了一个三阶段分割流程来优化MNR-UNet的训练。前两个阶段优先保证高精度和高灵敏度，第三阶段则专注于修复气道断裂，以平衡拓扑完整性和正确性。为进一步应对类别不平衡问题，我们引入了加权断裂感知损失函数（wBAL），以加强对困难样本的关注，通过惩罚断裂现象来延伸气道树的长度。此外，我们提出了分层评估框架，以提供更具临床意义的分析。在内部和公共数据集上的验证表明，我们的方法在检测更准确的气道体素和识别额外分支方面表现出优越性能，显著提升了气道拓扑完整性。代码将在论文发表后公开。