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FogBank: a single cell segmentation across multiple cell lines and image modalities

Overview of attention for article published in BMC Bioinformatics, December 2014
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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 (84th percentile)
  • Good Attention Score compared to outputs of the same age and source (78th percentile)

Mentioned by

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3 X users
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2 patents
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1 Facebook page

Citations

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66 Dimensions

Readers on

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102 Mendeley
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Title
FogBank: a single cell segmentation across multiple cell lines and image modalities
Published in
BMC Bioinformatics, December 2014
DOI 10.1186/s12859-014-0431-x
Pubmed ID
Authors

Joe Chalfoun, Michael Majurski, Alden Dima, Christina Stuelten, Adele Peskin, Mary Brady

Abstract

BackgroundMany cell lines currently used in medical research, such as cancer cells or stem cells, grow in confluent sheets or colonies. The biology of individual cells provide valuable information, thus the separation of touching cells in these microscopy images is critical for counting, identification and measurement of individual cells. Over-segmentation of single cells continues to be a major problem for methods based on morphological watershed due to the high level of noise in microscopy cell images. There is a need for a new segmentation method that is robust over a wide variety of biological images and can accurately separate individual cells even in challenging datasets such as confluent sheets or colonies.ResultsWe present a new automated segmentation method called FogBank that accurately separates cells when confluent and touching each other. This technique is successfully applied to phase contrast, bright field, fluorescence microscopy and binary images. The method is based on morphological watershed principles with two new features to improve accuracy and minimize over-segmentation.First, FogBank uses histogram binning to quantize pixel intensities which minimizes the image noise that causes over-segmentation. Second, FogBank uses a geodesic distance mask derived from raw images to detect the shapes of individual cells, in contrast to the more linear cell edges that other watershed-like algorithms produce.We evaluated the segmentation accuracy against manually segmented datasets using two metrics. FogBank achieved segmentation accuracy on the order of 0.75 (1 being a perfect match). We compared our method with other available segmentation techniques in term of achieved performance over the reference data sets. FogBank outperformed all related algorithms. The accuracy has also been visually verified on data sets with 14 cell lines across 3 imaging modalities leading to 876 segmentation evaluation images.ConclusionsFogBank produces single cell segmentation from confluent cell sheets with high accuracy. It can be applied to microscopy images of multiple cell lines and a variety of imaging modalities. The code for the segmentation method is available as open-source and includes a Graphical User Interface for user friendly execution.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 102 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 <1%
United States 1 <1%
Belgium 1 <1%
Unknown 99 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 23 23%
Researcher 23 23%
Student > Master 12 12%
Student > Bachelor 9 9%
Student > Doctoral Student 8 8%
Other 12 12%
Unknown 15 15%
Readers by discipline Count As %
Engineering 21 21%
Agricultural and Biological Sciences 20 20%
Biochemistry, Genetics and Molecular Biology 12 12%
Computer Science 10 10%
Physics and Astronomy 8 8%
Other 11 11%
Unknown 20 20%
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 02 January 2020.
All research outputs
#3,944,066
of 22,775,504 outputs
Outputs from BMC Bioinformatics
#1,501
of 7,276 outputs
Outputs of similar age
#55,857
of 352,738 outputs
Outputs of similar age from BMC Bioinformatics
#32
of 151 outputs
Altmetric has tracked 22,775,504 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 7,276 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. 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 352,738 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 84% of its contemporaries.
We're also able to compare this research output to 151 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.