Cube-Cut: Vertebral Body Segmentation in MRI-Data through Cubic-Shaped Divergences

Robert Schwarzenberg, Bernd Freisleben, Christopher Nimsky and Jan Egger

PLOS ONE, 2014, vol. 9, issue 4, 1-17

Abstract: 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 Δ. 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.

Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0093389

DOI: 10.1371/journal.pone.0093389

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