Medizinische Universität Graz - Research portal
Selected Publication:
SHR Neuro Cancer Cardio Lipid Metab Microb
Schwarzenberg, R; Freisleben, B; Nimsky, C; Egger, J.
Cube-cut: vertebral body segmentation in MRI-data through cubic-shaped divergences.
PLoS One. 2014; 9(4): e93389-e93389.
Doi: 10.1371/journal.pone.0093389
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- Leading authors Med Uni Graz
- Egger Jan
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- 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.
- Find related publications in this database (using NLM MeSH Indexing)
- Algorithms -
- Anatomy, Cross-Sectional - methods
- Humans -
- Image Interpretation, Computer-Assisted - methods
- Imaging, Three-Dimensional - methods
- Magnetic Resonance Imaging - methods
- Organ Size -
- Spine - anatomy & histology