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Segmentation of abdomen MR images using kernel graph cuts with shape priors

Overview of attention for article published in BioMedical Engineering OnLine, December 2013
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About this Attention Score

  • Average Attention Score compared to outputs of the same age
  • Above-average Attention Score compared to outputs of the same age and source (51st percentile)

Mentioned by

patent
2 patents

Citations

dimensions_citation
18 Dimensions

Readers on

mendeley
19 Mendeley
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Title
Segmentation of abdomen MR images using kernel graph cuts with shape priors
Published in
BioMedical Engineering OnLine, December 2013
DOI 10.1186/1475-925x-12-124
Pubmed ID
Authors

Qing Luo, Wenjian Qin, Tiexiang Wen, Jia Gu, Nikolas Gaio, Shifu Chen, Ling Li, Yaoqin Xie

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Japan 1 5%
Unknown 18 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 26%
Student > Master 5 26%
Researcher 2 11%
Student > Bachelor 2 11%
Professor > Associate Professor 2 11%
Other 2 11%
Unknown 1 5%
Readers by discipline Count As %
Computer Science 6 32%
Engineering 4 21%
Agricultural and Biological Sciences 3 16%
Medicine and Dentistry 2 11%
Chemistry 2 11%
Other 1 5%
Unknown 1 5%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 28 November 2023.
All research outputs
#7,485,894
of 22,880,230 outputs
Outputs from BioMedical Engineering OnLine
#207
of 823 outputs
Outputs of similar age
#92,394
of 307,321 outputs
Outputs of similar age from BioMedical Engineering OnLine
#17
of 39 outputs
Altmetric has tracked 22,880,230 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 823 research outputs from this source. They receive a mean Attention Score of 4.6. This one has gotten more attention than average, scoring higher than 62% 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 307,321 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 49th percentile – i.e., 49% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 39 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.