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Found 6 papers, 1 papers with code
Graph Refinement based Airway Extraction using Mean-Field Networks and Graph Neural Networks
Graph refinement, or the task of obtaining subgraphs of interest from over-complete graphs, can have many varied applications.
Extracting Tree-structures in CT data by Tracking Multiple Statistically Ranked Hypotheses
We propose to use statistical ranking of local hypotheses in constructing the MHT tree, which yields a probabilistic interpretation of scores across scales and helps alleviate the scale-dependence of MHT parameters.
Extraction of Airways using Graph Neural Networks
We present extraction of tree structures, such as airways, from image data as a graph refinement task.
Mean Field Network based Graph Refinement with application to Airway Tree Extraction
Performance of the method is compared with two methods: the first uses probability images from a trained voxel classifier with region growing, which is similar to one of the best performing methods at EXACT'09 airway challenge, and the second method is based on Bayesian smoothing on these probability images.
Extraction of Airways with Probabilistic State-space Models and Bayesian Smoothing
The evolution of individual branches is modelled using a process model and the observed data is incorporated into the update step of the Bayesian smoother using a measurement model that is based on a multi-scale blob detector.
Extraction of airway trees using multiple hypothesis tracking and template matching
The results show improvements in performance when compared to the original method and region growing on intensity images.
