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Detection and tracking of overlapping cell nuclei for large scale mitosis analyses

Overview of attention for article published in BMC Bioinformatics, April 2016
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3 tweeters

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28 Mendeley
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Title
Detection and tracking of overlapping cell nuclei for large scale mitosis analyses
Published in
BMC Bioinformatics, April 2016
DOI 10.1186/s12859-016-1030-9
Pubmed ID
Authors

Yingbo Li, France Rose, Florencia di Pietro, Xavier Morin, Auguste Genovesio

Abstract

Cell culture on printed micropatterns slides combined with automated fluorescent microscopy allows for extraction of tens of thousands of videos of small isolated growing cell clusters. The analysis of such large dataset in space and time is of great interest to the community in order to identify factors involved in cell growth, cell division or tissue formation by testing multiples conditions. However, cells growing on a micropattern tend to be tightly packed and to overlap with each other. Consequently, image analysis of those large dynamic datasets with no possible human intervention has proven impossible using state of the art automated cell detection methods. Here, we propose a fully automated image analysis approach to estimate the number, the location and the shape of each cell nucleus, in clusters at high throughput. The method is based on a robust fit of Gaussian mixture models with two and three components on each frame followed by an analysis over time of the fitting residual and two other relevant features. We use it to identify with high precision the very first frame containing three cells. This allows in our case to measure a cell division angle on each video and to construct division angle distributions for each tested condition. We demonstrate the accuracy of our method by validating it against manual annotation on about 4000 videos of cell clusters. The proposed approach enables the high throughput analysis of video sequences of isolated cell clusters obtained using micropatterns. It relies only on two parameters that can be set robustly as they reduce to the average cell size and intensity.

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

Geographical breakdown

Country Count As %
Unknown 28 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 36%
Student > Ph. D. Student 5 18%
Student > Master 4 14%
Student > Bachelor 3 11%
Student > Doctoral Student 1 4%
Other 2 7%
Unknown 3 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 9 32%
Computer Science 8 29%
Biochemistry, Genetics and Molecular Biology 2 7%
Physics and Astronomy 1 4%
Neuroscience 1 4%
Other 2 7%
Unknown 5 18%

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 31 May 2016.
All research outputs
#4,103,910
of 7,836,572 outputs
Outputs from BMC Bioinformatics
#2,397
of 3,473 outputs
Outputs of similar age
#145,852
of 268,814 outputs
Outputs of similar age from BMC Bioinformatics
#80
of 104 outputs
Altmetric has tracked 7,836,572 research outputs across all sources so far. This one is in the 27th percentile – i.e., 27% of other outputs scored the same or lower than it.
So far Altmetric has tracked 3,473 research outputs from this source. They receive a mean Attention Score of 5.0. This one is in the 20th percentile – i.e., 20% 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 268,814 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 104 others from the same source and published within six weeks on either side of this one. This one is in the 12th percentile – i.e., 12% of its contemporaries scored the same or lower than it.