ArticlePloS one2014
Cube-cut: vertebral body segmentation in MRI-data through cubic-shaped divergences.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- On computerized methods for spine analysis in MRI: a systematic review.International journal of computer assisted radiology and surgery · 2016Pooled it
- Trial
- Artificial Intelligence in Spinal Imaging: Current Status and Future Directions.International journal of environmental research and public health · 2022Review
- Artificial intelligence and machine learning in spine research.JOR spine · 2019Review
- Computed tomography data collection of the complete human mandible and valid clinical ground truth models.Scientific data · 2019Article
- Semi-Automatic Segmentation of Vertebral Bodies in MR Images of Human Lumbar Spines.Applied sciences (Basel, Switzerland) · 2018Article
- Machine Learning in Orthopedics: A Literature Review.Frontiers in bioengineering and biotechnology · 2018Review
- Algorithm guided outlining of 105 pancreatic cancer liver metastases in Ultrasound.Scientific reports · 2017Article
- Interactive Outlining of Pancreatic Cancer Liver Metastases in Ultrasound Images.Scientific reports · 2017Article
- Multi-Parameter Ensemble Learning for Automated Vertebral Body Segmentation in Heterogeneously Acquired Clinical MR Images.IEEE journal of translational engineering in health and medicine · 2017Article
- Vertebral body segmentation withSAGE open medicine · 2017Article
- Article
- Article
- Quantitative magnetic resonance imaging of the lumbar intervertebral discs.Quantitative imaging in medicine and surgery · 2016Review
- Shape complexes: the intersection of label orderings and star convexity constraints in continuous max-flow medical image segmentation.Journal of medical imaging (Bellingham, Wash.) · 2016Article
- Fully Automatic Localization and Segmentation of 3D Vertebral Bodies from CT/MR Images via a Learning-Based Method.PloS one · 2015Article
- Refinement-cut: user-guided segmentation algorithm for translational science.Scientific reports · 2014Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.