Segmentation of cerebral vascular networks from 3D angiographic data remains a challenge. Automation generally induces a high computational cost and possible errors, while interactive methods are hard to use due to the dimension and complexity of images. This article presents a compromise between both approaches, by using the concept of example-based segmentation. Segmentation examples of vascular structures are involved in a scheme relying on connected filtering, in order to obtain an interactive –but strongly assisted– segmentation method. This strategy, which uses component- trees in a non-standard fashion, leads to promising results, when applied on cerebral MR angiographic data.