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Detection and Segmentation of Cell Nuclei in Virtual Microscopy Images: A Minimum-Model Approach

Overview of attention for article published in Scientific Reports, July 2012
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  • Good Attention Score compared to outputs of the same age (75th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (51st percentile)

Mentioned by

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3 X users
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1 patent

Citations

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194 Dimensions

Readers on

mendeley
228 Mendeley
Title
Detection and Segmentation of Cell Nuclei in Virtual Microscopy Images: A Minimum-Model Approach
Published in
Scientific Reports, July 2012
DOI 10.1038/srep00503
Pubmed ID
Authors

Stephan Wienert, Daniel Heim, Kai Saeger, Albrecht Stenzinger, Michael Beil, Peter Hufnagl, Manfred Dietel, Carsten Denkert, Frederick Klauschen

Abstract

Automated image analysis of cells and tissues has been an active research field in medical informatics for decades but has recently attracted increased attention due to developments in computer and microscopy hardware and the awareness that scientific and diagnostic pathology require novel approaches to perform objective quantitative analyses of cellular and tissue specimens. Model-based approaches use a priori information on cell shape features to obtain the segmentation, which may introduce a bias favouring the detection of cell nuclei only with certain properties. In this study we present a novel contour-based "minimum-model" cell detection and segmentation approach that uses minimal a priori information and detects contours independent of their shape. This approach avoids a segmentation bias with respect to shape features and allows for an accurate segmentation (precision = 0.908; recall = 0.859; validation based on ∼8000 manually-labeled cells) of a broad spectrum of normal and disease-related morphological features without the requirement of prior training.

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 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 228 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 3 1%
United Kingdom 2 <1%
Mexico 2 <1%
United States 2 <1%
Brazil 1 <1%
Czechia 1 <1%
Canada 1 <1%
Italy 1 <1%
Denmark 1 <1%
Other 1 <1%
Unknown 213 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 66 29%
Researcher 38 17%
Student > Master 30 13%
Student > Bachelor 18 8%
Student > Doctoral Student 11 5%
Other 29 13%
Unknown 36 16%
Readers by discipline Count As %
Computer Science 61 27%
Engineering 47 21%
Agricultural and Biological Sciences 28 12%
Medicine and Dentistry 19 8%
Biochemistry, Genetics and Molecular Biology 9 4%
Other 21 9%
Unknown 43 19%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 21 September 2017.
All research outputs
#5,684,260
of 22,671,366 outputs
Outputs from Scientific Reports
#38,672
of 122,120 outputs
Outputs of similar age
#39,814
of 164,330 outputs
Outputs of similar age from Scientific Reports
#79
of 170 outputs
Altmetric has tracked 22,671,366 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 122,120 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 18.2. This one has gotten more attention than average, scoring higher than 68% 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 164,330 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 75% of its contemporaries.
We're also able to compare this research output to 170 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 51% of its contemporaries.