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

Gillette, Todd A.; Grefenstette, John J.

doi:10.1007/s12021-009-9053-2

Cited by 10 publications

(11 citation statements)

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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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“…However, TED is not spatially specific, while node locations are important in detecting morphological error or change. For example, when measuring quality, the double mistake of an extra branch and a missing branch extending from the same parent branch would be ignored by TED (see also Gillette and Grefenstette, 2009). The analogous situation for morphological plasticity would be the growth of one branch simultaneous with the retraction of another (i.e.…”

Section: Previous Relevant Workmentioning

confidence: 99%

The DIADEM Metric: Comparing Multiple Reconstructions of the Same Neuron

2011

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Digital reconstructions of neuronal morphology are used to study neuron function, development, and responses to various conditions. Although many measures exist to analyze differences between neurons, none is particularly suitable to compare the same arborizing structure over time (morphological change) or reconstructed by different people and/or software (morphological error). The metric introduced for the DIADEM (DIgital reconstruction of Axonal and DEndritic Morphology) Challenge quantifies the similarity between two reconstructions of the same neuron by matching the locations of bifurcations and terminations as well as their topology between the two reconstructed arbors. The DIADEM metric was specifically designed to capture the most critical aspects in automating neuronal reconstructions, and can function in feedback loops during algorithm development. During the Challenge, the metric scored the automated reconstructions of best-performing algorithms against manually traced gold standards over a representative data set collection. The metric was compared with direct quality assessments by neuronal reconstruction experts and with clocked human tracing time saved by automation. The results indicate that relevant morphological features were properly quantified in spite of subjectivity in the underlying image data and varying research goals. The DIADEM metric is freely released open source (http://diademchallenge.org) as a flexible instrument to measure morphological error or change in high-throughput reconstruction projects.

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“…Methods in this category often aim to find correspondences between two (neuron) trees, as well as to develop a tree distance to measure the quality of the resulting tree alignment. One important development in this direction is the use of a tree edit distance (TED) for aligning neuron trees [ 14 , 15 ]. The tree edit distance can be considered as an extension of the string-edit distance.…”

Section: Introductionmentioning

confidence: 99%

Metrics for comparing neuronal tree shapes based on persistent homology

Wang

Ascoli

et al. 2017

PLoS ONE

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As more and more neuroanatomical data are made available through efforts such as NeuroMorpho.Org and FlyCircuit.org, the need to develop computational tools to facilitate automatic knowledge discovery from such large datasets becomes more urgent. One fundamental question is how best to compare neuron structures, for instance to organize and classify large collection of neurons. We aim to develop a flexible yet powerful framework to support comparison and classification of large collection of neuron structures efficiently. Specifically we propose to use a topological persistence-based feature vectorization framework. Existing methods to vectorize a neuron (i.e, convert a neuron to a feature vector so as to support efficient comparison and/or searching) typically rely on statistics or summaries of morphometric information, such as the average or maximum local torque angle or partition asymmetry. These simple summaries have limited power in encoding global tree structures. Based on the concept of topological persistence recently developed in the field of computational topology, we vectorize each neuron structure into a simple yet informative summary. In particular, each type of information of interest can be represented as a descriptor function defined on the neuron tree, which is then mapped to a simple persistence-signature. Our framework can encode both local and global tree structure, as well as other information of interest (electrophysiological or dynamical measures), by considering multiple descriptor functions on the neuron. The resulting persistence-based signature is potentially more informative than simple statistical summaries (such as average/mean/max) of morphometric quantities—Indeed, we show that using a certain descriptor function will give a persistence-based signature containing strictly more information than the classical Sholl analysis. At the same time, our framework retains the efficiency associated with treating neurons as points in a simple Euclidean feature space, which would be important for constructing efficient searching or indexing structures over them. We present preliminary experimental results to demonstrate the effectiveness of our persistence-based neuronal feature vectorization framework.

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

Cited by 10 publications

References 11 publications

Neuron tracing in perspective

Neuron tracing in perspective

The DIADEM Metric: Comparing Multiple Reconstructions of the Same Neuron

Metrics for comparing neuronal tree shapes based on persistent homology

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