1. A method for modeling a cerebral vessel with a beginning and an end with an aneurysm from image data having a plurality of image pixels, comprising: a processor segmenting the aneurysm and cerebral vessel from a background into a segmentation mask; the processor determining the beginning and the end of the cerebral vessel; the processor extracting a local centerline between the beginning and end of the cerebral vessel up to the aneurysm by applying a measure of vesselness based on multi-scale cross-sectional vessel modeling; and the processor constructing a 3D cerebral vessel surface model up to the aneurysm wherein the extracting of the centerline includes the steps of: creating a graph of nodes and edges wherein nodes represent pixels in the plurality of pixels and assigning a cost to an edge, the cost of an edge being determined as a measure of vesselness calculated orthogonal to the edge; and determining the local center axis by calculating a minimal-cost path in the graph; wherein the measure of vesselness VM(x,y,z) for a pixel at coordinate (x,y,z) is related to a fit measure f i along a ray for a number of N rays according to an expression VM ⁡ ( x , y , z ) = 1 ∑ i = 1 N ⁢ f i ; and wherein a fit measure f i along a ray is a difference between a measured intensity profile I and a vessel model V according to an expression f i = arg ⁢ ⁢ min R , σ ⁢ ( γ 1 ⁢ ∑ x = 0 R ⁢  V i ⁡ ( x , R , σ ) - I i ⁡ ( x )  2 ⁢ f ⁡ ( x ) + γ 2 ⁢ ∑ x = R x B ⁢  V i ⁡ ( x , R , σ ) - I i  2 + γ ⁢ ⁢ 3 ⁢ ∑ x = x B x E ⁢  V i ⁡ ( x , R , σ ) - I i ⁡ ( x )  2 ⁢ g ⁡ ( x ) ) .