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Building generic anatomical models using virtual model cutting and iterative registration

Overview of attention for article published in BMC Medical Imaging, February 2010
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Mentioned by

patent
2 patents

Citations

dimensions_citation
1 Dimensions

Readers on

mendeley
23 Mendeley
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Title
Building generic anatomical models using virtual model cutting and iterative registration
Published in
BMC Medical Imaging, February 2010
DOI 10.1186/1471-2342-10-5
Pubmed ID
Authors

Mei Xiao, Jung Soh, Oscar Meruvia-Pastor, Eric Schmidt, Benedikt Hallgrímsson, Christoph W Sensen

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 4%
Unknown 22 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 26%
Student > Master 5 22%
Student > Ph. D. Student 4 17%
Professor 3 13%
Other 1 4%
Other 2 9%
Unknown 2 9%
Readers by discipline Count As %
Computer Science 5 22%
Medicine and Dentistry 5 22%
Engineering 5 22%
Agricultural and Biological Sciences 2 9%
Psychology 1 4%
Other 3 13%
Unknown 2 9%
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 13 February 2018.
All research outputs
#7,549,344
of 23,031,582 outputs
Outputs from BMC Medical Imaging
#107
of 606 outputs
Outputs of similar age
#49,463
of 166,516 outputs
Outputs of similar age from BMC Medical Imaging
#1
of 1 outputs
Altmetric has tracked 23,031,582 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 606 research outputs from this source. They receive a mean Attention Score of 2.1. This one has done well, scoring higher than 80% 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 166,516 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 22nd percentile – i.e., 22% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them