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Mendeley readers
Attention Score in Context
| Title |
A novel cell nuclei segmentation method for 3D C. elegans embryonic time-lapse images
|
|---|---|
| Published in |
BMC Bioinformatics, November 2013
|
| DOI | 10.1186/1471-2105-14-328 |
| Pubmed ID | |
| Authors |
Long Chen, Leanne Lai Hang Chan, Zhongying Zhao, Hong Yan |
| Abstract |
Recently a series of algorithms have been developed, providing automatic tools for tracing C. elegans embryonic cell lineage. In these algorithms, 3D images collected from a confocal laser scanning microscope were processed, the output of which is cell lineage with cell division history and cell positions with time. However, current image segmentation algorithms suffer from high error rate especially after 350-cell stage because of low signal-noise ratio as well as low resolution along the Z axis (0.5-1 microns). As a result, correction of the errors becomes a huge burden. These errors are mainly produced in the segmentation of nuclei. Thus development of a more accurate image segmentation algorithm will alleviate the hurdle for automated analysis of cell lineage. |
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 % |
|---|---|---|
| Norway | 1 | 50% |
| United Kingdom | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 2 | 5% |
| France | 1 | 3% |
| Brazil | 1 | 3% |
| Unknown | 33 | 89% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 10 | 27% |
| Researcher | 8 | 22% |
| Student > Master | 7 | 19% |
| Student > Doctoral Student | 2 | 5% |
| Other | 2 | 5% |
| Other | 3 | 8% |
| Unknown | 5 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 8 | 22% |
| Computer Science | 6 | 16% |
| Engineering | 6 | 16% |
| Biochemistry, Genetics and Molecular Biology | 4 | 11% |
| Physics and Astronomy | 4 | 11% |
| Other | 3 | 8% |
| Unknown | 6 | 16% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 18 May 2014.
All research outputs
#18,354,532
of 22,731,677 outputs
Outputs from BMC Bioinformatics
#6,300
of 7,266 outputs
Outputs of similar age
#227,870
of 302,097 outputs
Outputs of similar age from BMC Bioinformatics
#80
of 103 outputs
Altmetric has tracked 22,731,677 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,266 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 5th percentile – i.e., 5% 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 302,097 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 103 others from the same source and published within six weeks on either side of this one. This one is in the 9th percentile – i.e., 9% of its contemporaries scored the same or lower than it.