Purpose

We accelerate a pathline-based cardiovascular model building method by training machine learning models to directly predict vessel lumen surface points from computed tomography (CT) and magnetic resonance (MR) medical image data.

Methods

We formulate vessel lumen detection as a regression task using a polar coordiantes representation.

Results

Neural networks trained with our regression formulation allow predictions to be made with significantly higher accuracy than existing methods that identify the vessel lumen through binary pixel classification. The regression formulation enables machine learning models to be trained end-to-end for vessel lumen detection without post-processing steps that reduce accuracy.

Conclusion

By employing our models in a pathline-based cardiovascular model building pipeline we substantially reduce the manual segmentation effort required to build accurate cardiovascular models, and reduce the overall time required to perform patient-specific cardiovascular simulations. While our method is applied here for cardiovascular model building it is generally applicable to segmentation of tree-like and tubular structures from image data.

中文翻译：

---

神经网络血管腔回归用于心血管基于图像的建模中的自动腔横截面分割

目的

我们通过训练机器学习模型来加速基于路径的心血管模型构建方法，以直接从计算机断层扫描 (CT) 和磁共振 (MR) 医学图像数据中预测血管腔表面点。

方法

我们使用极坐标表示将血管腔检测制定为回归任务。

结果

使用我们的回归公式训练的神经网络与通过二进制像素分类识别血管腔的现有方法相比，能够以显着更高的准确度进行预测。回归公式使机器学习模型能够针对血管腔检测进行端到端训练，而无需降低准确性的后处理步骤。

结论

通过在基于路径的心血管模型构建管道中使用我们的模型，我们大大减少了构建准确心血管模型所需的手动分割工作，并减少了执行特定患者心血管模拟所需的总时间。虽然我们的方法在这里应用于心血管模型构建，但它通常适用于从图像数据中分割树状和管状结构。