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Mendeley readers
Attention Score in Context
Title |
3D cell nuclei segmentation based on gradient flow tracking
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Published in |
BMC Molecular and Cell Biology, September 2007
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DOI | 10.1186/1471-2121-8-40 |
Pubmed ID | |
Authors |
Gang Li, Tianming Liu, Ashley Tarokh, Jingxin Nie, Lei Guo, Andrew Mara, Scott Holley, Stephen TC Wong |
Abstract |
Reliable segmentation of cell nuclei from three dimensional (3D) microscopic images is an important task in many biological studies. We present a novel, fully automated method for the segmentation of cell nuclei from 3D microscopic images. It was designed specifically to segment nuclei in images where the nuclei are closely juxtaposed or touching each other. The segmentation approach has three stages: 1) a gradient diffusion procedure, 2) gradient flow tracking and grouping, and 3) local adaptive thresholding. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
Country | Count | As % |
---|---|---|
Japan | 1 | 100% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 182 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 5 | 3% |
United States | 5 | 3% |
France | 3 | 2% |
Germany | 3 | 2% |
Brazil | 1 | <1% |
Finland | 1 | <1% |
Slovenia | 1 | <1% |
Switzerland | 1 | <1% |
Japan | 1 | <1% |
Other | 1 | <1% |
Unknown | 160 | 88% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 60 | 33% |
Researcher | 47 | 26% |
Student > Master | 21 | 12% |
Professor > Associate Professor | 12 | 7% |
Student > Doctoral Student | 7 | 4% |
Other | 19 | 10% |
Unknown | 16 | 9% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 48 | 26% |
Computer Science | 45 | 25% |
Engineering | 23 | 13% |
Physics and Astronomy | 16 | 9% |
Biochemistry, Genetics and Molecular Biology | 15 | 8% |
Other | 15 | 8% |
Unknown | 20 | 11% |
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 23 November 2012.
All research outputs
#16,681,672
of 26,017,215 outputs
Outputs from BMC Molecular and Cell Biology
#726
of 1,254 outputs
Outputs of similar age
#72,621
of 85,759 outputs
Outputs of similar age from BMC Molecular and Cell Biology
#21
of 24 outputs
Altmetric has tracked 26,017,215 research outputs across all sources so far. This one is in the 33rd percentile – i.e., 33% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,254 research outputs from this source. They receive a mean Attention Score of 4.0. This one is in the 39th percentile – i.e., 39% 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 85,759 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 13th percentile – i.e., 13% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 24 others from the same source and published within six weeks on either side of this one. This one is in the 12th percentile – i.e., 12% of its contemporaries scored the same or lower than it.