volume and then visualized using direct volume rendering [@Kubisch2012]. Therefore, vessel visualization can take advantage of existing volume visualization techniques to improve shape and depth perception. However, representing such data as a volume is not the only approach used by domain experts, as volume datasets often include other tissues that have to be filtered before visualizing the vascular structures. Thus, several algorithms exist for extracting the vascular structures from volume datasets and render them by using different primitives (such as meshes or truncated cones) [@Gerig1993; @Hahn2001; @Oeltze2005]. Due to the different representations used and the particular shape of these structures, specific techniques were developed to improve perception in such models. Ritter et al [@Ritter2006] developed an illustrative technique which uses hatch lines [@Praun2001] on top of the vessels to encode occlusions between the different branches of the structure. Moreover, they also applied a stroke texture with a varying width depending on the distance to the viewer. Lawonn et al. [@Lawonn2015] presented a technique which generates illustrative shadows with support lines and silhouettes to improve perception on vascular structures. Moreover, they also proposed a 2D visualization in which distance was encoded by different hatching styles. Lichtenberg et al. [@Lichtenberg2017] presented a 