Nugget-Cut: A Segmentation Scheme for Spherically- and Elliptically-Shaped 3D Objects

In this paper, a segmentation method for spherically- and elliptically-shaped objects is presented. It utilizes a user-defined seed point to set up a directed 3D graph. The nodes of the 3D graph are obtained by sampling along rays that are sent through the surface points of a polyhedron. Additionally, several arcs and a parameter constrain the set of possible segmentations and enforce smoothness. After the graph has been constructed, the minimal cost closed set on the graph is computed via a polynomial time s-t cut, creating an optimal segmentation of the object. The presented method has been evaluated on 50 Magnetic Resonance Imaging (MRI) data sets with World Health Organization (WHO) grade IV gliomas (glioblastoma multiforme). The ground truth of the tumor boundaries were manually extracted by three clinical experts (neurological surgeons) with several years (> 6) of experience in resection of gliomas and afterwards compared with the automatic segmentation results of the proposed scheme yielding an average Dice Similarity Coefficient (DSC) of 80.37±8.93%. However, no segmentation method provides a perfect result, so additional editing on some slices was required, but these edits could be achieved quickly because the automatic segmentation provides a border that fits mostly to the desired contour. Furthermore, the manual segmentation by neurological surgeons took 2-32 minutes (mean: 8 minutes), in contrast to the automatic segmentation with our implementation that took less than 5 seconds.