| Title |
A Sensitive Thresholding Method for Confocal Laser Scanning Microscope Image Stacks of Microbial Biofilms
|
|---|---|
| Published in |
Scientific Reports, August 2018
|
| DOI | 10.1038/s41598-018-31012-5 |
| Pubmed ID | |
| Authors |
Ting L. Luo, Marisa C. Eisenberg, Michael A. L. Hayashi, Carlos Gonzalez-Cabezas, Betsy Foxman, Carl F. Marrs, Alexander H. Rickard |
| Abstract |
Biofilms are surface-attached microbial communities whose architecture can be captured with confocal microscopy. Manual or automatic thresholding of acquired images is often needed to help distinguish biofilm biomass from background noise. However, manual thresholding is subjective and current automatic thresholding methods can lead to loss of meaningful data. Here, we describe an automatic thresholding method designed for confocal fluorescent signal, termed the biovolume elasticity method (BEM). We evaluated BEM using confocal image stacks of oral biofilms grown in pooled human saliva. Image stacks were thresholded manually and automatically with three different methods; Otsu, iterative selection (IS), and BEM. Effects on biovolume, surface area, and number of objects detected indicated that the BEM was the least aggressive at removing signal, and provided the greatest visual and quantitative acuity of single cells. Thus, thresholding with BEM offers a sensitive, automatic, and tunable method to maintain biofilm architectural properties for subsequent analysis. |
X Demographics
The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Canada | 1 | 20% |
| United Kingdom | 1 | 20% |
| Netherlands | 1 | 20% |
| Unknown | 2 | 40% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 3 | 60% |
| Members of the public | 2 | 40% |
Mendeley readers
The data shown below were compiled from readership statistics for 60 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 60 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 15 | 25% |
| Student > Ph. D. Student | 8 | 13% |
| Student > Bachelor | 6 | 10% |
| Other | 5 | 8% |
| Student > Doctoral Student | 4 | 7% |
| Other | 7 | 12% |
| Unknown | 15 | 25% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 10 | 17% |
| Biochemistry, Genetics and Molecular Biology | 8 | 13% |
| Medicine and Dentistry | 7 | 12% |
| Engineering | 5 | 8% |
| Nursing and Health Professions | 2 | 3% |
| Other | 11 | 18% |
| Unknown | 17 | 28% |
Attention Score in Context
This research output has an Altmetric Attention Score of 10. 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 2018.
All research outputs
#3,347,784
of 23,298,349 outputs
Outputs from Scientific Reports
#28,871
of 125,929 outputs
Outputs of similar age
#69,035
of 335,756 outputs
Outputs of similar age from Scientific Reports
#897
of 3,564 outputs
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We're also able to compare this research output to 3,564 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.