Abstract: In the prevention and diagnosis of cerebrovascular-related diseases, vascular segmentation based on radiography is an important prerequisite for achieving accurate diagnosis. Former, artificial outlines based on two-dimensional images were considered time-consuming and laborious with low accuracy. Currently, semi-automatic or automatic vessel segmentation algorithms based on three-dimensional radiography have improved segmentation accuracy. This article focuses on two advanced algorithms of image processing methods in the field of brain vessel imaging: traditional algorithms and hybrid algorithms combined with deep learning. Between these algorithms, image processing based on traditional algorithms is more basic and common, whereas image processing based on deep learning is still gradually maturing. By enumerating the representative cerebrovascular segmentation and extraction algorithms based on radiography reported in recent years, the authors describe the utilized image information, accuracy, robustness, and efficiency of each algorithm. Moreover, the authors forecast the future development direction and research focus in the field of cerebrovascular segmentation on the basis of the in-depth understanding of the current situation.