The Tree-Edit-Distance, a Measure for Quantifying Neuronal Morphology

Heumann, Holger; Wittum, Gabriel

doi:10.1007/s12021-009-9051-4

Cited by 41 publications

(40 citation statements)

References 33 publications

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“…The power of these measures lies in the fact that, much more than any of the previously mentioned measures, they account for the characteristic connectivity and branching patterns of the neuronal trees. Another recently proposed measure to compute the (dis)similarity between any two neurons, known as the constrained tree-editdistance (97,98), takes this idea even a step further. Essentially, it computes the distance between two node-labeled (unordered) trees as the sum of weighted edit operations (label substitutions, node insertions, and deletions) needed to transform (match) one tree exactly into the other, minimized over all feasible edit sequences.…”

Section: Tree Quantificationmentioning

confidence: 99%

Neuron tracing in perspective

2010

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The study of the structure and function of neuronal cells and networks is of crucial importance in the endeavor to understand how the brain works. A key component in this process is the extraction of neuronal morphology from microscopic imaging data. In the past four decades, many computational methods and tools have been developed for digital reconstruction of neurons from images, with limited success. As witnessed by the growing body of literature on the subject, as well as the organization of challenging competitions in the field, the quest for a robust and fully automated system of more general applicability still continues. The aim of this work, is to contribute by surveying recent developments in the field for anyone interested in taking up the challenge. Relevant aspects discussed in the article include proposed image segmentation methods, quantitative measures of neuronal morphology, currently available software tools for various related purposes, and morphology databases. ' 2010 International Society for Advancement of Cytometry

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Section: Tree Quantificationmentioning

confidence: 99%

Neuron tracing in perspective

2010

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“…One of the best known metrics is TED [1], a method based on the tree-edit distance between unordered labeled tree-graphs. It measures the difference between two trees by counting the number of c 2013.…”

Section: Introductionmentioning

confidence: 99%

“…We extend this work by defining a new metric between trees. 1 The proposed method is based on the Elastic Shape Analysis Framework [5]. This framework was originally developed to compare shapes of curves in the Euclidean space.…”

Section: Introductionmentioning

confidence: 99%

Tree-like Shapes Distance Using the Elastic Shape Analysis Framework

Mottini

Descombes

Besse

2013

Procedings of the British Machine Vision Conference 2013

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The analysis and comparison of tree-like shapes is of great importance since many structures in nature can be described by them. In the field of biomedical imaging, trees have been used to describe structures such as neurons, blood vessels and lung airways. Since it is known that their morphology provides information on their functioning and allows the characterization of pathological states, it is of paramount importance to develop methods to analyze their shape and to quantify differences in structures.In this paper, we present a new method for comparing tree-like shapes that takes into account both topological and geometrical information. It is based on the Elastic Shape Analysis Framework, a framework originally designed for comparing shapes of 3D closed curves in Euclidean spaces. As a first application, we used our method for the comparison of axon morphology. The performance was tested on a group of 44 (20 normal and 24 mutant) 3D images, each containing one axonal tree. We have calculated inter and intra class distances between them and implemented a basic classification scheme. Results showed that the proposed method better distinguishes between the two populations than a pure topological metric. Furthermore, mean shapes can be obtained with this method.

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“…In "The Tree-edit-distance, a Measure for Quantifying Neuronal Morphology", Heumann and Wittum's use of the constrained tree-edit-distance between unordered trees on neuronal dendrites provides a new perspective on comparing neuronal morphologies (Heumann and Wittum 2009). The tree-edit-distance offers a more explicit measure of similarity between two neuronal trees that actually represents the topological and geometric changes that would need to be applied to one tree in order to create the second tree.…”

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confidence: 99%

“…Editorial Note The authors of the target article (Heumann and Wittum 2009) acknowledge that the commentary is correct and thank its authors. They also add that complexity issues have not been considered in the present paper, since there was no problem to run the code on the specified data.…”

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confidence: 99%

On Comparing Neuronal Morphologies with the Constrained Tree-edit-distance

Gillette

Grefenstette

2009

Neuroinform

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The constrained tree-edit-distance provides a computationally practical method for comparing morphologies directly without first extracting distributions of other metrics. The application of the constrained tree-edit-distance to hippocampal dendrites by Heumann and Wittum is reviewed and considered in the context of other applications and potential future uses. The method has been used on neuromuscular projection axons for comparisons of topology as well as on trees for comparing plant architectures with particular parameter sets that may inform future efforts in comparing dendritic morphologies. While clearly practical on a small scale, testing and extrapolation of run-times raise questions as to the practicality of the constrained tree-edit-distance for large-scale data mining projects. However, other more efficient algorithms may make use of it as a gold standard for direct morphological comparison.

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The Tree-Edit-Distance, a Measure for Quantifying Neuronal Morphology

Cited by 41 publications

References 33 publications

Neuron tracing in perspective

Neuron tracing in perspective

Tree-like Shapes Distance Using the Elastic Shape Analysis Framework

On Comparing Neuronal Morphologies with the Constrained Tree-edit-distance

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