Spatiotemporal Disentanglement of Arteriovenous Malformations in Digital Subtraction Angiography

arXiv:2402.09636v1 Announce Type: cross
Abstract: 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 …

abstract arxiv clinicians cs.cv digital dsa eess.iv imaging interpretation true type