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Automated quality control in image segmentation: application to the UK Biobank cardiovascular magnetic resonance imaging study

Overview of attention for article published in Critical Reviews in Diagnostic Imaging, March 2019
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (79th percentile)
  • Good Attention Score compared to outputs of the same age and source (73rd percentile)

Mentioned by

twitter
17 X users

Citations

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93 Dimensions

Readers on

mendeley
164 Mendeley
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Title
Automated quality control in image segmentation: application to the UK Biobank cardiovascular magnetic resonance imaging study
Published in
Critical Reviews in Diagnostic Imaging, March 2019
DOI 10.1186/s12968-019-0523-x
Pubmed ID
Authors

Robert Robinson, Vanya V. Valindria, Wenjia Bai, Ozan Oktay, Bernhard Kainz, Hideaki Suzuki, Mihir M. Sanghvi, Nay Aung, José Miguel Paiva, Filip Zemrak, Kenneth Fung, Elena Lukaschuk, Aaron M. Lee, Valentina Carapella, Young Jin Kim, Stefan K. Piechnik, Stefan Neubauer, Steffen E. Petersen, Chris Page, Paul M. Matthews, Daniel Rueckert, Ben Glocker

X Demographics

X Demographics

The data shown below were collected from the profiles of 17 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 164 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 164 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 37 23%
Researcher 32 20%
Student > Master 20 12%
Student > Bachelor 10 6%
Student > Doctoral Student 6 4%
Other 21 13%
Unknown 38 23%
Readers by discipline Count As %
Engineering 43 26%
Computer Science 30 18%
Medicine and Dentistry 22 13%
Neuroscience 4 2%
Biochemistry, Genetics and Molecular Biology 4 2%
Other 15 9%
Unknown 46 28%
Attention Score in Context

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 18 June 2020.
All research outputs
#3,608,507
of 25,806,080 outputs
Outputs from Critical Reviews in Diagnostic Imaging
#197
of 1,388 outputs
Outputs of similar age
#75,228
of 366,398 outputs
Outputs of similar age from Critical Reviews in Diagnostic Imaging
#4
of 15 outputs
Altmetric has tracked 25,806,080 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,388 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.3. This one has done well, scoring higher than 85% 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 366,398 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 79% of its contemporaries.
We're also able to compare this research output to 15 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 73% of its contemporaries.