Direction-Dependent Level Set Segmentation of Cerebrovascular Structures

Exact cerebrovascular segmentations based on high resolution 3D anatomical datasets are required for many clinical applications. A general problem of most vessel segmentation methods is the insufficient delineation of small vessels, which are often represented by rather low intensities and high surface curvatures. This paper describes an improved direction-dependent level set approach for the cerebrovascular segmentation. The proposed method utilizes the direction information of the eigenvectors computed by vesselness filters for adjusting the weights of the internal energy depending on the location. The basic idea of this is to weight the internal energy lower in case the gradient of the level set is comparable to the direction of the eigenvector extracted by the vesselness filter. A quantitative evaluation of the proposed method based on three clinical Time-of-Flight MRA datasets with available manual segmentations using the Tanimoto coefficient showed that a mean improvement compared to the initial segmentation of 0.081 is achieved, while the corresponding level set segmentation without integration of direction information does not lead to satisfying results. In summary, the proposed method enables an improved delineation of small vessels, especially of those represented by low intensities and high surface curvatures.