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

TL;DR

This paper presents a semi-automatic, graph-based method for segmenting brain fiber bundles in DTI data, incorporating automatic cost function determination to enhance robustness and patient-specific accuracy for neurosurgical planning.

Contribution

It introduces an automatic approach for defining the cost function in fiber bundle segmentation, reducing manual intervention and improving adaptability to individual patient data.

Findings

Effective segmentation of fiber bundles demonstrated

Automatic cost function improves robustness

Method supports neurosurgical planning

Abstract

Diffusion Tensor Imaging (DTI) allows estimating the position, orientation and dimension of bundles of nerve pathways. This non-invasive imaging technique takes advantage of the diffusion of water molecules and determines the diffusion coefficients for every voxel of the data set. The identification of the diffusion coefficients and the derivation of information about fiber bundles is of major interest for planning and performing neurosurgical interventions. To minimize the risk of neural deficits during brain surgery as tumor resection (e.g. glioma), the segmentation and integration of the results in the operating room is of prime importance. In this contribution, a robust and efficient graph-based approach for segmentating tubular fiber bundles in the human brain is presented. To define a cost function, the fractional anisotropy (FA) is used, derived from the DTI data, but this value may differ from patient to patient. Besides manually definining seed regions describing the structure of interest, additionally a manual definition of the cost function by the user is necessary. To improve the approach the contribution introduces a solution for automatically determining the cost function by using different 3D masks for each individual data set.