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A generic classification-based method for segmentation of nuclei in 3D images of early embryos

Overview of attention for article published in BMC Bioinformatics, January 2014
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Mentioned by

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3 tweeters

Citations

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31 Dimensions

Readers on

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58 Mendeley
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Title
A generic classification-based method for segmentation of nuclei in 3D images of early embryos
Published in
BMC Bioinformatics, January 2014
DOI 10.1186/1471-2105-15-9
Pubmed ID
Authors

Jaza Gul-Mohammed, Ignacio Arganda-Carreras, Philippe Andrey, Vincent Galy, Thomas Boudier

Abstract

Studying how individual cells spatially and temporally organize within the embryo is a fundamental issue in modern developmental biology to better understand the first stages of embryogenesis. In order to perform high-throughput analyses in three-dimensional microscopic images, it is essential to be able to automatically segment, classify and track cell nuclei. Many 3D/4D segmentation and tracking algorithms have been reported in the literature. Most of them are specific to particular models or acquisition systems and often require the fine tuning of parameters.

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 58 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Japan 1 2%
Unknown 57 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 18 31%
Researcher 11 19%
Student > Master 8 14%
Professor > Associate Professor 5 9%
Student > Doctoral Student 4 7%
Other 8 14%
Unknown 4 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 15 26%
Biochemistry, Genetics and Molecular Biology 12 21%
Computer Science 10 17%
Engineering 5 9%
Medicine and Dentistry 3 5%
Other 9 16%
Unknown 4 7%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 16 June 2014.
All research outputs
#9,152,816
of 14,573,111 outputs
Outputs from BMC Bioinformatics
#3,735
of 5,420 outputs
Outputs of similar age
#137,590
of 260,689 outputs
Outputs of similar age from BMC Bioinformatics
#287
of 418 outputs
Altmetric has tracked 14,573,111 research outputs across all sources so far. This one is in the 24th percentile – i.e., 24% of other outputs scored the same or lower than it.
So far Altmetric has tracked 5,420 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 22nd percentile – i.e., 22% of its peers scored the same or lower than it.
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 260,689 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 35th percentile – i.e., 35% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 418 others from the same source and published within six weeks on either side of this one. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.