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Survey statistics of automated segmentations applied to optical imaging of mammalian cells

Overview of attention for article published in BMC Bioinformatics, October 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (80th percentile)
  • High Attention Score compared to outputs of the same age and source (82nd percentile)

Mentioned by

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9 X users
patent
1 patent
facebook
1 Facebook page

Citations

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45 Dimensions

Readers on

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76 Mendeley
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Title
Survey statistics of automated segmentations applied to optical imaging of mammalian cells
Published in
BMC Bioinformatics, October 2015
DOI 10.1186/s12859-015-0762-2
Pubmed ID
Authors

Peter Bajcsy, Antonio Cardone, Joe Chalfoun, Michael Halter, Derek Juba, Marcin Kociolek, Michael Majurski, Adele Peskin, Carl Simon, Mylene Simon, Antoine Vandecreme, Mary Brady

Abstract

The goal of this survey paper is to overview cellular measurements using optical microscopy imaging followed by automated image segmentation. The cellular measurements of primary interest are taken from mammalian cells and their components. They are denoted as two- or three-dimensional (2D or 3D) image objects of biological interest. In our applications, such cellular measurements are important for understanding cell phenomena, such as cell counts, cell-scaffold interactions, cell colony growth rates, or cell pluripotency stability, as well as for establishing quality metrics for stem cell therapies. In this context, this survey paper is focused on automated segmentation as a software-based measurement leading to quantitative cellular measurements. We define the scope of this survey and a classification schema first. Next, all found and manually filteredpublications are classified according to the main categories: (1) objects of interests (or objects to be segmented), (2) imaging modalities, (3) digital data axes, (4) segmentation algorithms, (5) segmentation evaluations, (6) computational hardware platforms used for segmentation acceleration, and (7) object (cellular) measurements. Finally, all classified papers are converted programmatically into a set of hyperlinked web pages with occurrence and co-occurrence statistics of assigned categories. The survey paper presents to a reader: (a) the state-of-the-art overview of published papers about automated segmentation applied to optical microscopy imaging of mammalian cells, (b) a classification of segmentation aspects in the context of cell optical imaging, (c) histogram and co-occurrence summary statistics about cellular measurements, segmentations, segmented objects, segmentation evaluations, and the use of computational platforms for accelerating segmentation execution, and (d) open research problems to pursue. The novel contributions of this survey paper are: (1) a new type of classification of cellular measurements and automated segmentation, (2) statistics about the published literature, and (3) a web hyperlinked interface to classification statistics of the surveyed papers at https://isg.nist.gov/deepzoomweb/resources/survey/index.html .

X Demographics

X Demographics

The data shown below were collected from the profiles of 9 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 76 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 1%
Unknown 75 99%

Demographic breakdown

Readers by professional status Count As %
Researcher 19 25%
Student > Ph. D. Student 17 22%
Student > Master 12 16%
Student > Doctoral Student 6 8%
Student > Bachelor 6 8%
Other 9 12%
Unknown 7 9%
Readers by discipline Count As %
Computer Science 16 21%
Agricultural and Biological Sciences 14 18%
Engineering 12 16%
Biochemistry, Genetics and Molecular Biology 7 9%
Medicine and Dentistry 5 7%
Other 13 17%
Unknown 9 12%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 11 January 2023.
All research outputs
#4,186,781
of 24,046,191 outputs
Outputs from BMC Bioinformatics
#1,503
of 7,495 outputs
Outputs of similar age
#53,000
of 283,432 outputs
Outputs of similar age from BMC Bioinformatics
#23
of 134 outputs
Altmetric has tracked 24,046,191 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,495 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has done well, scoring higher than 79% 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 283,432 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 80% of its contemporaries.
We're also able to compare this research output to 134 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.