Cube-cut: vertebral body segmentation in MRI-data through cubic-shaped divergences.
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.
Department of Mathematics and Computer Science, University of Marburg, Marburg, Germany.
Robert
R
Schwarzenberg
Department of Mathematics and Computer Science, University of Marburg, Marburg, Germany.
Bernd
B
Freisleben
Department of Neurosurgery, University Hospital of Marburg, Marburg, Germany.
Christopher
C
Nimsky
Department of Mathematics and Computer Science, University of Marburg, Marburg, Germany; Department of Neurosurgery, University Hospital of Marburg, Marburg, Germany.
Jan
J
Egger
D000465:Algorithms; D019412:Anatomy, Cross-Sectional / Q000379:methods*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009929:Organ Size; D013131:Spine / Q000033:anatomy & histology*