论文标题
自动分割大动脉进行计算血液动力学评估
Automatic Segmentation of the Great Arteries for Computational Hemodynamic Assessment
论文作者
论文摘要
背景：计算流体动力学（CFD）越来越多地用于评估先天性心脏病（CHD）患者的血流条件。这需要患者特异性解剖结构，通常从分段的3D心血管磁共振（CMR）图像获得。但是，细分是耗时的，需要专家输入。这项研究旨在开发和验证一种用于分割主动脉和肺动脉（PAS）的机器学习方法（ML）方法。 方法：回顾性选择了90名CHD患者。手动分段3D CMR图像以获得地面真相（GT）背景，主动脉和PA标签。这些被用来训练和优化U-NET模型。分段性能主要使用骰子评分评估。使用半自动网络和仿真管道从GT和ML分割设置了CFD模拟。计算压力和速度场，并为每个容器对计算平均平均百分比误差（MAPE）。次要观察者（SO）将测试数据集分段以评估观察者间的变异性。弗里德曼测试用于比较分段指标和流场误差。 结果：模型的骰子得分（ML与GT）为主动脉的0.945，PAS为0.885。与观察者间骰子分数（SO VS GT）和ML与ML VS的差异，因此在主动脉或PAS中，骰子得分在统计学上没有统计学意义。主动脉的压力和速度的ML与GT MAPE分别为10.1％和4.1％，PAS分别为14.6％和6.3％。观察者间（SO VS GT）和ML与SO压力和速度映射的幅度与ML与GT相似。 结论：所提出的方法可以成功分割CFD的大容器，其误差类似于观察者间的变异性。这减少了CFD分析所需的时间和精力，使其对常规临床使用更具吸引力。
Background: Computational fluid dynamics (CFD) is increasingly used to assess blood flow conditions in patients with congenital heart disease (CHD). This requires patient-specific anatomy, usually obtained from segmented 3D cardiovascular magnetic resonance (CMR) images. However, segmentation is time-consuming and needs expert input. This study aims to develop and validate a machine learning (ML) method for segmentation of the aorta and pulmonary arteries (PAs) for CFD studies. Methods: 90 CHD patients were retrospectively selected for this study. 3D CMR images were manually segmented to obtain ground-truth (GT) background, aorta and PA labels. These were used to train and optimize a U-Net model. Segmentation performance was primarily evaluated using Dice score. CFD simulations were set up from GT and ML segmentations using a semi-automatic meshing and simulation pipeline. Pressure and velocity fields were computed, and a mean average percentage error (MAPE) was calculated for each vessel pair. A secondary observer (SO) segmented the test dataset to assess inter-observer variability. Friedman tests were used to compare segmentation metrics and flow field errors. Results: The model's Dice score (ML vs GT) was 0.945 for the aorta and 0.885 for the PAs. Differences with the inter-observer Dice score (SO vs GT) and ML vs SO Dice scores were not statistically significant for either aorta or PAs. The ML vs GT MAPEs for pressure and velocity in the aorta were 10.1% and 4.1% respectively, and for the PAs 14.6% and 6.3%, respectively. Inter-observer (SO vs GT) and ML vs SO pressure and velocity MAPEs were of a similar magnitude to ML vs GT. Conclusions: The proposed method can successfully segment the great vessels for CFD, with errors similar to inter-observer variability. This reduces the time and effort needed for CFD analysis, making it more attractive for routine clinical use.
