用局部全局注意力提升低对比度冠脉的分割精度
A Neighborhood Attention Transformer Network for Enhanced 3D Segmentation of the Left Anterior Descending Artery
- 引入邻域注意力模块，兼顾细节与长程依赖
- 在20例真实CT上达45.64% Dice，边界误差降低2.96mm
- 适合放射治疗中精细心脏结构分割任务
背景：在3D自由呼吸、非对比增强CT中准确分割左前降支（LAD）动脉对胸腔放疗中保护心脏剂量至关重要。LAD管径极细、软组织对比度差且个体差异大，即使人工勾画也存在显著观察者间不一致性，凸显其边界模糊性。目的：提出一种基于Transformer的框架，通过局部-全局上下文建模与不确定性引导优化，提升低对比度、数据不平衡条件下的LAD分割性能。方法：提出NA-UNETR模型，其邻域注意力（NA）与扩张型邻域注意力（DiNA）模块协同捕捉细微结构与长程上下文。鉴于标注数据稀缺，模型先在1,000个冠状动脉通用CTA数据上预训练，再使用LoRA进行参数高效微调，在20例机构自由呼吸CT上完成适配。采用动态平衡的复合Dice-Focal与豪斯多夫损失，提升重叠度与边界精度。结果：NA-UNETR达到45.64% Dice、38.16 mm HD95、10.01 mm ASD，相较nnU-Net提升3.10个百分点，较Swin UNETR降低2.96 mm HD95，边界精度最优且中心线更稳定。在ImageCAS数据集上达79.49% Dice、8.89 mm HD95、1.02 mm ASD。消融实验验证了残差块、可变核及不确定性加权损失的有效性。结论：NA-UNETR有效平衡局部精度与全局上下文，为放疗规划中亚结构级心脏分割提供高效计算方案。
原文摘要 · Abstract (English)
Background: Accurate segmentation of the Left Anterior Descending (LAD) artery in 3D free-breathing, non-contrast CT is critical for cardiac dose sparing in thoracic radiotherapy. The LAD is extremely small, has poor soft-tissue contrast, and varies substantially across patients; even manual contours show limited inter-observer agreement, underscoring the ambiguity of the vessel boundaries. Purpose: To develop a transformer-based framework that improves LAD delineation in low-contrast, imbalanced CT through local-global context modeling and uncertainty-guided optimization. Methods: We propose NA-UNETR, a 3D transformer-based segmentation model whose Neighborhood Attention (NA) and Dilated NA (DiNA) blocks jointly capture fine structural detail and long-range context. Given the scarcity of annotated LAD data, the model is pretrained on 1,000 CTA volumes of general coronary anatomy and fine-tuned with LoRA-based parameter-efficient adaptation on 20 free-breathing institutional CT scans. A composite Dice-Focal and Hausdorff loss, dynamically balanced via homoscedastic uncertainty, improves overlap and boundary accuracy. Results: NA-UNETR reached 45.64% Dice, 38.16 mm HD95, and 10.01 mm ASD, improving Dice by 3.10 percentage points over nnU-Net and reducing HD95 by 2.96 mm relative to Swin UNETR, with the strongest boundary accuracy among all models and improved centerline stability. On ImageCAS it achieved 79.49% Dice, 8.89 mm HD95, and 1.02 mm ASD. Ablations confirmed that residual blocks, variable kernels, and uncertainty-weighted loss each contributed. Conclusions: NA-UNETR balances local precision and global context for thin, low-contrast LAD structures, offering a computationally efficient framework for substructure-level cardiac segmentation in radiotherapy planning.
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