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Lesion segmentation in breast ultrasound images using the optimized marked watershed method

Overview of attention for article published in BioMedical Engineering OnLine, June 2021
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  • Average Attention Score compared to outputs of the same age
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
2 X users

Citations

dimensions_citation
11 Dimensions

Readers on

mendeley
31 Mendeley
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Title
Lesion segmentation in breast ultrasound images using the optimized marked watershed method
Published in
BioMedical Engineering OnLine, June 2021
DOI 10.1186/s12938-021-00891-7
Pubmed ID
Authors

Xiaoyan Shen, He Ma, Ruibo Liu, Hong Li, Jiachuan He, Xinran Wu

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 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 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 31 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 10%
Other 2 6%
Lecturer 2 6%
Student > Master 2 6%
Student > Bachelor 1 3%
Other 4 13%
Unknown 17 55%
Readers by discipline Count As %
Engineering 6 19%
Computer Science 6 19%
Nursing and Health Professions 1 3%
Unknown 18 58%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 08 June 2021.
All research outputs
#15,154,377
of 23,308,124 outputs
Outputs from BioMedical Engineering OnLine
#404
of 834 outputs
Outputs of similar age
#244,236
of 447,409 outputs
Outputs of similar age from BioMedical Engineering OnLine
#7
of 14 outputs
Altmetric has tracked 23,308,124 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 834 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 48th percentile – i.e., 48% of its peers scored the same or lower than it.
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 447,409 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 14 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 50% of its contemporaries.