ArticleNeuroinformatics2009
The tree-edit-distance, a measure for quantifying neuronal morphology.
Article in Neuroinformatics, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Novel metrics reveal new structure and unappreciated heterogeneity in Caenorhabditis elegans development.PLoS computational biology · 2023Article
- Semi-Automated Quantitative Evaluation of Neuron Developmental Morphology In Vitro Using the Change-Point Test.Neuroinformatics · 2023Article
- Fitting Splines to Axonal Arbors Quantifies Relationship Between Branch Order and Geometry.Frontiers in neuroinformatics · 2021Article
- A Systematic Evaluation of Interneuron Morphology Representations for Cell Type Discrimination.Neuroinformatics · 2020Article
- Density Visualization Pipeline: A Tool for Cellular and Network Density Visualization and Analysis.Frontiers in computational neuroscience · 2020Article
- Article
- BlastNeuron for Automated Comparison, Retrieval and Clustering of 3D Neuron Morphologies.Neuroinformatics · 2015Article
- Topological characterization of neuronal arbor morphology via sequence representation: II--global alignment.BMC bioinformatics · 2015Article
- From Curves to Trees: A Tree-like Shapes Distance Using the Elastic Shape Analysis Framework.Neuroinformatics · 2015Article
- NetMets: software for quantifying and visualizing errors in biological network segmentation.BMC bioinformatics · 2012Article
- Models and simulation of 3D neuronal dendritic trees using Bayesian networks.Neuroinformatics · 2011Article
- The DIADEM metric: comparing multiple reconstructions of the same neuron.Neuroinformatics · 2011Article
- Automated reconstruction of neuronal morphology: an overview.Brain research reviews · 2011Review
- Identifying neuronal lineages of Drosophila by sequence analysis of axon tracts.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2010Article
- On comparing neuronal morphologies with the constrained tree-edit-distance.Neuroinformatics · 2009Review
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Authors and funding
2 authors.
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Abstract
The shape of neuronal cells strongly resembles botanical trees or roots of plants. To analyze and compare these complex three-dimensional structures it is important to develop suitable methods. We review the so called tree-edit-distance known from theoretical computer science and use this distance to define dissimilarity measures for neuronal cells. This measure intrinsically respects the tree-shape. It compares only those parts of two dendritic trees that have similar position in the whole tree. Therefore it can be interpreted as a generalization of methods using vector valued measures. Moreover, we show that our new measure, together with cluster analysis, is a suitable method for analyzing three-dimensional shape of hippocampal and cortical cells.
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