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

PCG-Cut: Graph Driven Segmentation of the Prostate Central Gland