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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Region-based progressive localization of cell nuclei in microscopic images with data adaptive modeling
|
|---|---|
| Published in |
BMC Bioinformatics, June 2013
|
| DOI | 10.1186/1471-2105-14-173 |
| Pubmed ID | |
| Authors |
Yang Song, Weidong Cai, Heng Huang, Yue Wang, David Dagan Feng, Mei Chen |
| Abstract |
Segmenting cell nuclei in microscopic images has become one of the most important routines in modern biological applications. With the vast amount of data, automatic localization, i.e. detection and segmentation, of cell nuclei is highly desirable compared to time-consuming manual processes. However, automated segmentation is challenging due to large intensity inhomogeneities in the cell nuclei and the background. |
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.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 50% |
| Norway | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 50% |
| Members of the public | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 2 | 6% |
| United Kingdom | 1 | 3% |
| Austria | 1 | 3% |
| Unknown | 30 | 88% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 12 | 35% |
| Researcher | 5 | 15% |
| Student > Master | 5 | 15% |
| Student > Postgraduate | 2 | 6% |
| Professor | 2 | 6% |
| Other | 4 | 12% |
| Unknown | 4 | 12% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 11 | 32% |
| Engineering | 6 | 18% |
| Agricultural and Biological Sciences | 4 | 12% |
| Biochemistry, Genetics and Molecular Biology | 3 | 9% |
| Neuroscience | 2 | 6% |
| Other | 2 | 6% |
| Unknown | 6 | 18% |
Attention Score in Context
This research output has an Altmetric Attention Score of 9. 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 04 October 2013.
All research outputs
#3,718,542
of 22,711,242 outputs
Outputs from BMC Bioinformatics
#1,419
of 7,259 outputs
Outputs of similar age
#32,207
of 193,471 outputs
Outputs of similar age from BMC Bioinformatics
#28
of 109 outputs
Altmetric has tracked 22,711,242 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,259 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. 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 193,471 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 83% of its contemporaries.
We're also able to compare this research output to 109 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 74% of its contemporaries.