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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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3 tweeters

Citations

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

Geographical breakdown

Country Count As %
Japan 1 2%
Unknown 60 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 18 30%
Researcher 12 20%
Student > Master 8 13%
Professor > Associate Professor 5 8%
Student > Bachelor 4 7%
Other 8 13%
Unknown 6 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 15 25%
Biochemistry, Genetics and Molecular Biology 12 20%
Computer Science 11 18%
Engineering 5 8%
Medicine and Dentistry 3 5%
Other 9 15%
Unknown 6 10%

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
#16,646,949
of 21,358,901 outputs
Outputs from BMC Bioinformatics
#5,660
of 6,928 outputs
Outputs of similar age
#217,177
of 304,867 outputs
Outputs of similar age from BMC Bioinformatics
#294
of 361 outputs
Altmetric has tracked 21,358,901 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 6,928 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 13th percentile – i.e., 13% 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 304,867 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 25th percentile – i.e., 25% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 361 others from the same source and published within six weeks on either side of this one. This one is in the 11th percentile – i.e., 11% of its contemporaries scored the same or lower than it.