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The tree-edit-distance, a measure for quantifying neuronal morphology

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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Correspondence to Gabriel Wittum.

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Open Access This article is published under license to BioMed Central Ltd. This is an Open Access article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Heumann, H., Wittum, G. The tree-edit-distance, a measure for quantifying neuronal morphology. BMC Neurosci 10, P89 (2009). https://doi.org/10.1186/1471-2202-10-S1-P89

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Keywords

  • Animal Model
  • Cluster Analysis
  • Computer Science
  • Neuronal Cell
  • Theoretical Computer