修复肺气道断点，让CT分割更完整准确
RepAir: A Framework for Airway Segmentation and Discontinuity Correction in CT
- 三阶段流程：先分割，再找断点，最后用1D分类器判断真实分支
- 在健康与病理性数据上均优于现有方法，断点修复率更高
- 适合做肺部疾病量化分析的研究者和临床医生
从胸部CT中精确分割气道对肺部定量分析至关重要，但人工标注不现实，且多数基于U-Net的自动化方法会产生断裂的气道结构，影响生物标志物提取。本文提出RepAir，一种三阶段3D气道分割框架，结合nnU-Net网络与解剖先验拓扑修正。分割网络生成初始气道掩码后，基于骨架的算法识别潜在断点并提出连接候选；随后1D卷积分类器判断哪些候选连接为真实解剖分支，而非伪连接或阻塞路径。我们在两个数据集上评估：ATM'22（主要来自健康受试者）和AeroPath（包含严重气道病变扫描）。在两个数据集上，RepAir在体素级与拓扑指标上均优于现有3D U-Net方法（如Bronchinet和NaviAirway），生成更完整、解剖一致性更强的气道树，同时保持高分割精度。
原文摘要 · Abstract (English)
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.
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