↓ Skip to main content

Impact of image segmentation on high-content screening data quality for SK-BR-3 cells

Overview of attention for article published in BMC Bioinformatics, September 2007
Altmetric Badge

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 (89th percentile)
  • High Attention Score compared to outputs of the same age and source (86th percentile)

Mentioned by

blogs
1 blog
f1000
1 research highlight platform

Citations

dimensions_citation
22 Dimensions

Readers on

mendeley
50 Mendeley
citeulike
2 CiteULike
connotea
1 Connotea
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Impact of image segmentation on high-content screening data quality for SK-BR-3 cells
Published in
BMC Bioinformatics, September 2007
DOI 10.1186/1471-2105-8-340
Pubmed ID
Authors

Andrew A Hill, Peter LaPan, Yizheng Li, Steve Haney

Abstract

High content screening (HCS) is a powerful method for the exploration of cellular signalling and morphology that is rapidly being adopted in cancer research. HCS uses automated microscopy to collect images of cultured cells. The images are subjected to segmentation algorithms to identify cellular structures and quantitate their morphology, for hundreds to millions of individual cells. However, image analysis may be imperfect, especially for "HCS-unfriendly" cell lines whose morphology is not well handled by current image segmentation algorithms. We asked if segmentation errors were common for a clinically relevant cell line, if such errors had measurable effects on the data, and if HCS data could be improved by automated identification of well-segmented cells.

Mendeley readers

The data shown below were compiled from readership statistics for 50 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 2 4%
United States 2 4%
Netherlands 1 2%
Spain 1 2%
Unknown 44 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 18 36%
Researcher 13 26%
Student > Doctoral Student 7 14%
Student > Master 5 10%
Student > Postgraduate 2 4%
Other 4 8%
Unknown 1 2%
Readers by discipline Count As %
Agricultural and Biological Sciences 17 34%
Computer Science 14 28%
Medicine and Dentistry 7 14%
Mathematics 2 4%
Biochemistry, Genetics and Molecular Biology 2 4%
Other 6 12%
Unknown 2 4%

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 24 September 2018.
All research outputs
#1,518,478
of 13,543,688 outputs
Outputs from BMC Bioinformatics
#621
of 5,058 outputs
Outputs of similar age
#20,683
of 210,362 outputs
Outputs of similar age from BMC Bioinformatics
#47
of 339 outputs
Altmetric has tracked 13,543,688 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,058 research outputs from this source. They receive a mean Attention Score of 4.9. This one has done well, scoring higher than 87% 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 210,362 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 89% of its contemporaries.
We're also able to compare this research output to 339 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 86% of its contemporaries.