用深度学习自动分割主动脉夹层，提升诊断效率与准确性
Advanced Deep Learning Techniques for Automated Segmentation of Type B Aortic Dissections
- 设计四种基于3D U-Net和Swin-UnetR的分割流程
- 对真腔、假腔和血栓的分割Dice系数分别达0.91、0.88、0.47
- 适合心血管影像分析与手术规划研究人员参考
主动脉夹层是危及生命的血管疾病，需从CTA图像中精确分割真腔（TL）、假腔（FL）及假腔血栓（FLT）以实现有效管理。人工分割耗时且结果差异大，亟需自动化方案。本研究构建了四种基于深度学习的分段流程：单步模型、串行模型、串行多任务模型和集成模型，采用3D U-Net与Swin-UnetR架构。使用100例回顾性CTA图像，按训练（n=80）、验证（n=10）、测试（n=10）划分。通过Dice系数与豪斯多夫距离评估性能。结果表明，本方法分割精度显著提升：真腔Dice为0.91±0.07，假腔为0.88±0.18，假腔血栓为0.47±0.25，优于姚等（1）的0.78±0.20、0.68±0.18、0.25±0.31。结论：所提流程可准确分割类型B主动脉夹层特征，支持形态参数提取，助力随访与治疗规划。
原文摘要 · Abstract (English)
Purpose: Aortic dissections are life-threatening cardiovascular conditions requiring accurate segmentation of true lumen (TL), false lumen (FL), and false lumen thrombosis (FLT) from CTA images for effective management. Manual segmentation is time-consuming and variable, necessitating automated solutions. Materials and Methods: We developed four deep learning-based pipelines for Type B aortic dissection segmentation: a single-step model, a sequential model, a sequential multi-task model, and an ensemble model, utilizing 3D U-Net and Swin-UnetR architectures. A dataset of 100 retrospective CTA images was split into training (n=80), validation (n=10), and testing (n=10). Performance was assessed using the Dice Coefficient and Hausdorff Distance. Results: Our approach achieved superior segmentation accuracy, with Dice Coefficients of 0.91 $\pm$ 0.07 for TL, 0.88 $\pm$ 0.18 for FL, and 0.47 $\pm$ 0.25 for FLT, outperforming Yao et al. (1), who reported 0.78 $\pm$ 0.20, 0.68 $\pm$ 0.18, and 0.25 $\pm$ 0.31, respectively. Conclusion: The proposed pipelines provide accurate segmentation of TBAD features, enabling derivation of morphological parameters for surveillance and treatment planning
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