  Prostate cancer is the most abundant cancer in men, with over 200,000
expected new cases and around 28,000 deaths in 2012 in the US alone. In this
study, the segmentation results for the prostate central gland (PCG) in MR
scans are presented. The aim of this research study is to apply a graph-based
algorithm to automated segmentation (i.e. delineation) of organ limits for the
prostate central gland. The ultimate goal is to apply automated segmentation
approach to facilitate efficient MR-guided biopsy and radiation treatment
planning. The automated segmentation algorithm used is graph-driven based on a
spherical template. Therefore, rays are sent through the surface points of a
polyhedron to sample the graph's nodes. After graph construction - which only
requires the center of the polyhedron defined by the user and located inside
the prostate center gland - the minimal cost closed set on the graph is
computed via a polynomial time s-t-cut, which results in the segmentation of
the prostate center gland's boundaries and volume. The algorithm has been
realized as a C++ modul within the medical research platform MeVisLab and the
ground truth of the central gland boundaries were manually extracted by
clinical experts (interventional radiologists) with several years of experience
in prostate treatment. For evaluation the automated segmentations of the
proposed scheme have been compared with the manual segmentations, yielding an
average Dice Similarity Coefficient (DSC) of 78.94 +/- 10.85%.
