Segmentation of Arteriovenous Malformation Based on Weighted Breadth-First Search of Vascular Skeleton

Abstract

Cerebral arteriovenous malformations (AVM) are prone to rupture, which will lead to life-threatening conditions. Because of the complexity and high mortality and disability rate of AVM, it has been a severe problem in surgery for many years. In this paper, we propose a new method of AVM location and segmentation based on graph theory. A weighted breadth-first search tree is created from the result of vascular skeletonization, and the AVM is automatically detected and extracted. The feeding arteries, draining veins and the AVM nidus are segmented according to the topological structure of the vessel. We evaluate the proposed method on clinical data sets and achieve an average accuracy of 95.14%, sensitivity of 82.28% and specificity of 94.88%. The results show that our method is effective and is helpful for the treatment of vascular interventional surgery.