Although Digital Subtraction Angiography (DSA) is the most important imaging
for visualizing cerebrovascular anatomy, its interpretation by clinicians
remains difficult. This is particularly true when treating arteriovenous
malformations (AVMs), where entangled vasculature connecting arteries and veins
needs to be carefully identified.The presented method aims to enhance DSA image
series by highlighting critical information via automatic classification of
vessels using a combination of two learning models: An unsupervised machine
learning method based on Independent Component Analysis that decomposes the
phases of flow and a convolutional neural network that automatically delineates
the vessels in image space. The proposed method was tested on clinical DSA
images series and demonstrated efficient differentiation between arteries and
veins that provides a viable solution to enhance visualizations for clinical
use.

通过使用两种学习模型：基于独立成分分析的无监督机器学习方法和基于卷积神经网络的自动细分图像空间中的血管，提出的方法在 DSA 影像序列中通过突出关键信息进行自动分类血管来增强 DSA 图像序列，通过高效区分动脉和静脉为临床使用的可行解决方案。