Continuous and accurate segmentation of airways in chest CT images is essential for preoperative planning and real-time bronchoscopy navigation. Despite advances in deep learning for medical image segmentation, maintaining airway continuity remains a challenge, particularly due to intra-class imbalance between large and small branches and blurred CT scan details. To address these challenges, we propose a progressive curriculum learning pipeline and a Scale-Enhanced U-Net (SE-UNet) to enhance segmentation continuity. Specifically, our progressive curriculum learning pipeline consists of three stages: extracting main airways, identifying small airways, and repairing discontinuities. The cropping sampling strategy in each stage reduces feature interference between airways of different scales, effectively addressing the challenge of intra-class imbalance. In the third training stage, we present an Adaptive Topology-Responsive Loss (ATRL) to guide the network to focus on airway continuity. The progressive training pipeline shares the same SE-UNet, integrating multi-scale inputs and Detail Information Enhancers (DIEs) to enhance information flow and effectively capture the intricate details of small airways. Additionally, we propose a robust airway tree parsing method and hierarchical evaluation metrics to provide more clinically relevant and precise analysis. Experiments on both in-house and public datasets demonstrate that our method outperforms existing approaches, significantly improving the accuracy of small airways and the completeness of the airway tree. The code will be released upon publication.

翻译：胸部CT图像中气道连续且准确的分割对于术前规划和实时支气管镜导航至关重要。尽管深度学习在医学图像分割领域取得了进展，保持气道连续性仍然是一项挑战，尤其由于大分支与小分支之间的类内不平衡以及CT扫描细节模糊所致。为解决这些挑战，我们提出了一种渐进式课程学习流程和尺度增强U-Net（SE-UNet）以提升分割连续性。具体而言，我们的渐进式课程学习流程包含三个阶段：提取主气道、识别小气道以及修复不连续区域。每个阶段中的裁剪采样策略减少了不同尺度气道间的特征干扰，有效应对了类内不平衡的挑战。在第三训练阶段，我们提出了一种自适应拓扑响应损失（ATRL）以引导网络关注气道连续性。该渐进训练流程共享相同的SE-UNet，通过整合多尺度输入和细节信息增强器（DIEs）来增强信息流，有效捕捉小气道的精细结构。此外，我们提出了一种鲁棒的气道树解析方法和分层评估指标，以提供更具临床相关性和精确性的分析。在内部和公共数据集上的实验表明，我们的方法优于现有方案，显著提升了小气道的准确性和气道树的完整性。代码将在论文发表时开源。