放疗圈看过来，这个模型把左前降支动脉分割做得更准，比nnU-Net和Swin UNETR都强，还用了LoRA省显存，值得试试。
NA-UNETR是一种基于Transformer的3D分割模型，专门用于自由呼吸非增强CT中左前降支（LAD）动脉的精细分割。该模型在1000个CTA体积上预训练，并通过LoRA在20个CT扫描上微调，使用Dice-Focal与Hausdorff组合损失，经同方差不确定性动态平衡。在内部数据集上，NA-UNETR达到45.64% Dice、38.16 mm HD95和10.01 mm ASD，Dice较nnU-Net提升3.10个百分点，HD95较Swin UNETR降低2.96 mm。在ImageCAS上获得79.49% Dice、8.89 mm HD95和1.02 mm ASD。消融实验证实残差块、可变卷积核和不确定性加权损失均有效。
A Neighborhood Attention Transformer Network for Enhanced 3D Segmentation of the Left Anterior Descending Artery
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.