A workflow to process 3D+time microscopy images of developing organisms and reconstruct their cell lineage

Overview of attention for article published in Nature Communications, February 2016
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (92nd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (57th percentile)

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32 tweeters

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51 Mendeley
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1 CiteULike
Title
A workflow to process 3D+time microscopy images of developing organisms and reconstruct their cell lineage
Published in
Nature Communications, February 2016
DOI 10.1038/ncomms9674
Pubmed ID
Authors

Emmanuel Faure, Thierry Savy, Barbara Rizzi, Camilo Melani, Olga Stašová, Dimitri Fabrèges, Róbert Špir, Mark Hammons, Róbert Čúnderlík, Gaëlle Recher, Benoît Lombardot, Louise Duloquin, Ingrid Colin, Jozef Kollár, Sophie Desnoulez, Pierre Affaticati, Benoît Maury, Adeline Boyreau, Jean-Yves Nief, Pascal Calvat, Philippe Vernier, Monique Frain, Georges Lutfalla, Yannick Kergosien, Pierre Suret, Mariana Remešíková, René Doursat, Alessandro Sarti, Karol Mikula, Nadine Peyriéras, Paul Bourgine

Abstract

The quantitative and systematic analysis of embryonic cell dynamics from in vivo 3D+time image data sets is a major challenge at the forefront of developmental biology. Despite recent breakthroughs in the microscopy imaging of living systems, producing an accurate cell lineage tree for any developing organism remains a difficult task. We present here the BioEmergences workflow integrating all reconstruction steps from image acquisition and processing to the interactive visualization of reconstructed data. Original mathematical methods and algorithms underlie image filtering, nucleus centre detection, nucleus and membrane segmentation, and cell tracking. They are demonstrated on zebrafish, ascidian and sea urchin embryos with stained nuclei and membranes. Subsequent validation and annotations are carried out using Mov-IT, a custom-made graphical interface. Compared with eight other software tools, our workflow achieved the best lineage score. Delivered in standalone or web service mode, BioEmergences and Mov-IT offer a unique set of tools for in silico experimental embryology.

Twitter Demographics

The data shown below were collected from the profiles of 32 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 51 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
France 2 4%
Germany 1 2%
Switzerland 1 2%
Slovakia 1 2%
Slovenia 1 2%
United Kingdom 1 2%
United States 1 2%
Netherlands 1 2%
Unknown 42 82%

Demographic breakdown

Readers by professional status Count As %
Researcher 15 29%
Student > Master 10 20%
Student > Ph. D. Student 9 18%
Student > Doctoral Student 6 12%
Student > Bachelor 4 8%
Other 7 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 29 57%
Biochemistry, Genetics and Molecular Biology 9 18%
Engineering 4 8%
Mathematics 2 4%
Computer Science 2 4%
Other 5 10%

Attention Score in Context

This research output has an Altmetric Attention Score of 20. 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 February 2017.
All research outputs
#360,717
of 7,408,468 outputs
Outputs from Nature Communications
#4,257
of 11,245 outputs
Outputs of similar age
#22,240
of 283,106 outputs
Outputs of similar age from Nature Communications
#348
of 819 outputs
Altmetric has tracked 7,408,468 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 11,245 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 41.2. This one has gotten more attention than average, scoring higher than 62% 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 283,106 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 92% of its contemporaries.
We're also able to compare this research output to 819 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 57% of its contemporaries.