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Cellular quantitative analysis of neuroblastoma tumor and splitting overlapping cells

Overview of attention for article published in BMC Bioinformatics, August 2014
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Title
Cellular quantitative analysis of neuroblastoma tumor and splitting overlapping cells
Published in
BMC Bioinformatics, August 2014
DOI 10.1186/1471-2105-15-272
Pubmed ID
Authors

Siamak Tafavogh, Daniel R Catchpoole, Paul J Kennedy

Abstract

Neuroblastoma Tumor (NT) is one of the most aggressive types of infant cancer. Essential to accurate diagnosis and prognosis is cellular quantitative analysis of the tumor. Counting enormous numbers of cells under an optical microscope is error-prone. There is therefore an urgent demand from pathologists for robust and automated cell counting systems. However, the main challenge in developing these systems is the inability of them to distinguish between overlapping cells and single cells, and to split the overlapping cells. We address this challenge in two stages by: 1) distinguishing overlapping cells from single cells using the morphological differences between them such as area, uniformity of diameters and cell concavity; and 2) splitting overlapping cells into single cells. We propose a novel approach by using the dominant concave regions of cells as markers to identify the overlap region. We then find the initial splitting points at the critical points of the concave regions by decomposing the concave regions into their components such as arcs, chords and edges, and the distance between the components is analyzed using the developed seed growing technique. Lastly, a shortest path determination approach is developed to determine the optimum splitting route between two candidate initial splitting points.

X Demographics

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The data shown below were collected from the profiles of 4 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 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Italy 1 4%
Unknown 22 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 35%
Student > Master 5 22%
Other 2 9%
Professor 2 9%
Researcher 2 9%
Other 3 13%
Unknown 1 4%
Readers by discipline Count As %
Computer Science 7 30%
Engineering 4 17%
Agricultural and Biological Sciences 4 17%
Medicine and Dentistry 3 13%
Unspecified 1 4%
Other 2 9%
Unknown 2 9%
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 05 September 2014.
All research outputs
#14,783,222
of 22,759,618 outputs
Outputs from BMC Bioinformatics
#5,040
of 7,273 outputs
Outputs of similar age
#126,981
of 230,877 outputs
Outputs of similar age from BMC Bioinformatics
#83
of 117 outputs
Altmetric has tracked 22,759,618 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 7,273 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 26th percentile – i.e., 26% 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 230,877 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 117 others from the same source and published within six weeks on either side of this one. This one is in the 26th percentile – i.e., 26% of its contemporaries scored the same or lower than it.