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Dendritic tree extraction from noisy maximum intensity projection images in C. elegans.

Overview of attention for article published in BioMedical Engineering OnLine, June 2014
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About this Attention Score

  • Among the highest-scoring outputs from this source (#39 of 335)
  • Good Attention Score compared to outputs of the same age (66th percentile)
  • Good Attention Score compared to outputs of the same age and source (70th percentile)

Mentioned by

twitter
3 tweeters

Citations

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5 Dimensions

Readers on

mendeley
19 Mendeley
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Title
Dendritic tree extraction from noisy maximum intensity projection images in C. elegans.
Published in
BioMedical Engineering OnLine, June 2014
DOI 10.1186/1475-925x-13-74
Pubmed ID
Authors

Greenblum A, Sznitman R, Fua P, Arratia PE, Oren M, Podbilewicz B, Sznitman J

Abstract

Maximum Intensity Projections (MIP) of neuronal dendritic trees obtained from confocal microscopy are frequently used to study the relationship between tree morphology and mechanosensory function in the model organism C. elegans. Extracting dendritic trees from noisy images remains however a strenuous process that has traditionally relied on manual approaches. Here, we focus on automated and reliable 2D segmentations of dendritic trees following a statistical learning framework.

Twitter Demographics

The data shown below were collected from the profiles of 3 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 19 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 26%
Researcher 4 21%
Professor 4 21%
Librarian 2 11%
Student > Master 1 5%
Other 2 11%
Unknown 1 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 6 32%
Engineering 4 21%
Biochemistry, Genetics and Molecular Biology 3 16%
Neuroscience 2 11%
Medicine and Dentistry 1 5%
Other 1 5%
Unknown 2 11%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 27 February 2015.
All research outputs
#1,170,481
of 4,811,984 outputs
Outputs from BioMedical Engineering OnLine
#39
of 335 outputs
Outputs of similar age
#46,292
of 143,628 outputs
Outputs of similar age from BioMedical Engineering OnLine
#3
of 10 outputs
Altmetric has tracked 4,811,984 research outputs across all sources so far. This one has received more attention than most of these and is in the 63rd percentile.
So far Altmetric has tracked 335 research outputs from this source. They receive a mean Attention Score of 1.8. This one has done well, scoring higher than 84% 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 143,628 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 66% of its contemporaries.
We're also able to compare this research output to 10 others from the same source and published within six weeks on either side of this one. This one has scored higher than 7 of them.