  The glioblastoma multiforme is the most common malignant primary brain tumor
and is one of the highest malignant human neoplasms. During the course of
disease, the evaluation of tumor volume is an essential part of the clinical
follow-up. However, manual segmentation for acquisition of tumor volume is a
time-consuming process. In this paper, a new approach for the automatic
segmentation and volume determination of glioblastomas (glioblastoma
multiforme) is presented and evaluated. The approach uses a user-defined seed
point inside the glioma to set up a directed 3D graph. The nodes of the graph
are obtained by sampling along rays that are sent through the surface points of
a polyhedron. After the graph has been constructed, the minimal s-t cut is
calculated to separate the glioblastoma from the background. For evaluation, 12
Magnetic Resonance Imaging (MRI) data sets were manually segmented slice by
slice, by neurosurgeons with several years of experience in the resection of
gliomas. Afterwards, the manual segmentations were compared with the results of
the presented approach via the Dice Similarity Coefficient (DSC). For a better
assessment of the DSC results, the manual segmentations of the experts were
also compared with each other and evaluated via the DSC. In addition, the 12
data sets were segmented once again by one of the neurosurgeons after a period
of two weeks, to also measure the intra-physician deviation of the DSC.
