Purpose: Coronary artery disease (CAD) is known to have high prevalence, high disability and mortality. The incidence and mortality of cardiovascular disease are also gradually increasing worldwide. Therefore, our paper proposes to use a more efficient image processing method to extract accurate vascular structures from vascular images by combining computer vision and deep learning.

Method: Our proposed segmentation of coronary angiography images based on PSPNet network was compared with FCN, and analyzed and discussed the experimental results using three evaluation indicators of precision, recall and Fl-score. Aiming at the complex and changeable structure of coronary angiography images and over-fitting or parameter structure destruction, we implemented the parallel multi-scale convolutional neural network model using PSPNet, using small sample transfer learning that limits parameter learning method.

Results: The accuracy of our technique proposed in this paper is 0.957. The accuracy of PSPNet is 26.75% higher than the traditional algorithm and 4.59% higher than U-Net. The average segmentation accuracy of the PSPNet model using transfer learning on the test set increased from 0.926 to 0.936, the sensitivity increased from 0.846 to 0.865, and the specificity increased from 0.921 to 0.949. The segmentation effect in this paper is closest to the segmentation result of the human expert, and is smoother than that of U-Net segmentation.

Conclusion: The PSPNet network reduces manual interaction in diagnosis, reduces dependence on medical personnel, improves the efficiency of disease diagnosis, and provides auxiliary strategies for subsequent medical diagnosis systems based on cardiac coronary angiography.

目的：众所周知，冠状动脉疾病（CAD）的患病率高，残疾和死亡率高。全世界心血管疾病的发病率和死亡率也在逐渐增加。因此，我们提出使用一种更有效的图像处理方法，通过结合计算机视觉和深度学习从血管图像中提取准确的血管结构。

方法：将我们提出的基于PSPNet网络的冠状动脉造影图像分割与FCN进行比较，并使用精度，召回率和Fl评分这三个评估指标对实验结果进行分析和讨论。针对冠状动脉血管造影图像的复杂和多变的结构以及过度拟合或参数结构破坏，我们使用限制参数学习方法的小样本传递学习方法，使用PSPNet实现了并行多尺度卷积神经网络模型。

结果：本文提出的技术的准确性为0.957。PSPNet的精度比传统算法高26.75％，比U-Net高4.59％。在测试集上使用转移学习的PSPNet模型的平均分割精度从0.926增至0.936，灵敏度从0.846增至0.865，特异性从0.921增至0.949。本文的分割效果最接近人类专家的分割结果，并且比U-Net分割更平滑。

结论： PSPNet网络减少了诊断中的人工交互，减少了对医务人员的依赖，提高了疾病诊断的效率，并为后续基于心脏冠状动脉造影的医学诊断系统提供了辅助策略。