%0 Journal Article
%T Cube-Cut: Vertebral Body Segmentation in MRI-Data through Cubic-Shaped Divergences
%A Robert Schwarzenberg
%A Bernd Freisleben
%A Christopher Nimsky
%A Jan Egger
%J PLOS ONE
%D 2014
%I Public Library of Science (PLoS)
%R 10.1371/journal.pone.0093389
%X In this article, we present a graph-based method using a cubic template for volumetric segmentation of vertebrae in magnetic resonance imaging (MRI) acquisitions. The user can define the degree of deviation from a regular cube via a smoothness value ¦¤. The Cube-Cut algorithm generates a directed graph with two terminal nodes (s-t-network), where the nodes of the graph correspond to a cubic-shaped subset of the image¡¯s voxels. The weightings of the graph¡¯s terminal edges, which connect every node with a virtual source s or a virtual sink t, represent the affinity of a voxel to the vertebra (source) and to the background (sink). Furthermore, a set of infinite weighted and non-terminal edges implements the smoothness term. After graph construction, a minimal s-t-cut is calculated within polynomial computation time, which splits the nodes into two disjoint units. Subsequently, the segmentation result is determined out of the source-set. A quantitative evaluation of a C++ implementation of the algorithm resulted in an average Dice Similarity Coefficient (DSC) of 81.33% and a running time of less than a minute.
%U http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0093389