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NeuroMorph: A Toolset for the Morphometric Analysis and Visualization of 3D Models Derived from Electron Microscopy Image Stacks

Overview of attention for article published in Neuroinformatics, September 2014
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (81st percentile)
  • High Attention Score compared to outputs of the same age and source (83rd percentile)

Mentioned by

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2 X users
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2 patents

Citations

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64 Dimensions

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107 Mendeley
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Title
NeuroMorph: A Toolset for the Morphometric Analysis and Visualization of 3D Models Derived from Electron Microscopy Image Stacks
Published in
Neuroinformatics, September 2014
DOI 10.1007/s12021-014-9242-5
Pubmed ID
Authors

Anne Jorstad, Biagio Nigro, Corrado Cali, Marta Wawrzyniak, Pascal Fua, Graham Knott

Abstract

Serialelectron microscopy imaging is crucial for exploring the structure of cells and tissues. The development of block face scanning electron microscopy methods and their ability to capture large image stacks, some with near isotropic voxels, is proving particularly useful for the exploration of brain tissue. This has led to the creation of numerous algorithms and software for segmenting out different features from the image stacks. However, there are few tools available to view these results and make detailed morphometric analyses on all, or part, of these 3D models. We have addressed this issue by constructing a collection of software tools, called NeuroMorph, with which users can view the segmentation results, in conjunction with the original image stack, manipulate these objects in 3D, and make measurements of any region. This approach to collecting morphometric data provides a faster means of analysing the geometry of structures, such as dendritic spines and axonal boutons. This bridges the gap that currently exists between rapid reconstruction techniques, offered by computer vision research, and the need to collect measurements of shape and form from segmented structures that is currently done using manual segmentation methods.

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 107 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 2 2%
Japan 1 <1%
Sweden 1 <1%
Unknown 103 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 20 19%
Researcher 19 18%
Student > Master 11 10%
Student > Doctoral Student 8 7%
Professor 7 7%
Other 17 16%
Unknown 25 23%
Readers by discipline Count As %
Neuroscience 24 22%
Agricultural and Biological Sciences 21 20%
Computer Science 10 9%
Medicine and Dentistry 9 8%
Engineering 4 4%
Other 11 10%
Unknown 28 26%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 8. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 07 September 2021.
All research outputs
#4,098,167
of 22,764,165 outputs
Outputs from Neuroinformatics
#66
of 404 outputs
Outputs of similar age
#45,037
of 251,167 outputs
Outputs of similar age from Neuroinformatics
#1
of 6 outputs
Altmetric has tracked 22,764,165 research outputs across all sources so far. Compared to these this one has done well and is in the 81st percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 404 research outputs from this source. They receive a mean Attention Score of 4.5. This one has done well, scoring higher than 83% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 251,167 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 81% of its contemporaries.
We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them