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Diffusion Tensor Imaging (DTI) allows estimating the position, orientationand dimension of bundles of nerve pathways. This non-invasive imaging techniquetakes advantage of the diffusion of water molecules and determines thediffusion coefficients for every voxel of the data set. The identification ofthe diffusion coefficients and the derivation of information about fiberbundles is of major interest for planning and performing neurosurgicalinterventions. To minimize the risk of neural deficits during brain surgery astumor resection (e.g. glioma), the segmentation and integration of the resultsin the operating room is of prime importance. In this contribution, a robustand efficient graph-based approach for segmentating tubular fiber bundles inthe human brain is presented. To define a cost function, the fractionalanisotropy (FA) is used, derived from the DTI data, but this value may differfrom patient to patient. Besides manually definining seed regions describingthe structure of interest, additionally a manual definition of the costfunction by the user is necessary. To improve the approach the contributionintroduces a solution for automatically determining the cost function by usingdifferent 3D masks for each individual data set.

A Semi-Automatic Graph-Based Approach for Determining the Boundary of Eloquent Fiber Bundles in the Human Brain