提出边界增强损失函数，提升肺部小气道分割精度与拓扑一致性。
Boundary-Emphasized Weight Maps for Distal Airway Segmentation
- 基于边界权重图与自适应优化策略，强化边界体素分类。
- 在ATM22和AIIB23数据集上提升拓扑相关指标，小气道分割更准确。
- 适合需要高精度结构保持的医学图像分割任务。
从肺部CT扫描中自动分割气道对肺部疾病诊断与监测至关重要。尽管已有进展，但漏分、断裂和类别不平衡问题仍存在，尤其在小气道和拓扑保持方面。本文提出边界增强损失（BEL），通过基于边界的权重图与自适应权重优化策略，强化边界体素的识别，减少误分类，改善拓扑结构并提升结构一致性，尤其适用于远端气道分支。在ATM22和AIIB23数据集上评估显示，BEL优于基线损失函数，显著提升拓扑相关指标，整体性能相当。定性结果表明，BEL能更好捕捉细微解剖结构，降低小气道分割误差。这些成果证明BEL是医学影像中精确且拓扑增强型气道分割的有力方案。
原文摘要 · Abstract (English)
Automated airway segmentation from lung CT scans is vital for diagnosing and monitoring pulmonary diseases. Despite advancements, challenges like leakage, breakage, and class imbalance persist, particularly in capturing small airways and preserving topology. We propose the Boundary-Emphasized Loss (BEL), which enhances boundary preservation using a boundary-based weight map and an adaptive weight refinement strategy. Unlike centerline-based approaches, BEL prioritizes boundary voxels to reduce misclassification, improve topology, and enhance structural consistency, especially on distal airway branches. Evaluated on ATM22 and AIIB23, BEL outperforms baseline loss functions, achieving higher topology-related metrics and comparable overall-based measures. Qualitative results further highlight BEL's ability to capture fine anatomical details and reduce segmentation errors, particularly in small airways. These findings establish BEL as a promising solution for accurate and topology-enhancing airway segmentation in medical imaging.
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