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A quantitative method to measure biofilm removal efficiency from complex biomaterial surfaces using SEM and image analysis

Vyas, N., Sammons, R. L., Addison, O., Dehghani, H., Walmsley, A. D.·2016·Scientific Reports
DOI 10.1038/srep32694PMC PMC5013386
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Abstract

Biofilm accumulation on biomaterial surfaces is a major health concern and significant research efforts are directed towards producing biofilm resistant surfaces and developing biofilm removal techniques. To accurately evaluate biofilm growth and disruption on surfaces, accurate methods which give quantitative information on biofilm area are needed, as current methods are indirect and inaccurate. We demonstrate the use of machine learning algorithms to segment biofilm from scanning electron microscopy images. A case study showing disruption of biofilm from rough dental implant surfaces using cavitation bubbles from an ultrasonic scaler is used to validate the imaging and analysis protocol developed. Streptococcus mutans biofilm was disrupted from sandblasted, acid etched (SLA) Ti discs and polished Ti discs. Significant biofilm removal occurred due to cavitation from ultrasonic scaling (p < 0.001). The mean sensitivity and specificity values for segmentation of the SLA surface images were 0.80 ± 0.18 and 0.62 ± 0.20 respectively and 0.74 ± 0.13 and 0.86 ± 0.09 respectively for polished surfaces. Cavitation has potential to be used as a novel way to clean dental implants. This imaging and analysis method will be of value to other researchers and manufacturers wishing to study biofilm growth and removal.

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Figure 1 SEM images of the clean surfaces produced in the study, imaged before biofilm colonisation. (a) Polished surface (b) SLA surface.
Figure 1 SEM images of the clean surfaces produced in the study, imaged before biofilm colonisation. (a) Polished surface (b) SLA surface.CC BY · PubMed Central
Figure 2 (a) Method developed to segment biofilm from the surface in SEM images. (b) Close-up showing image before and after pre-processing – outliers were removed to smooth scratches on the surface, for better segmentation. (c) Overlay of the segmented area (blue) on the SEM image, showing accurate detection of biofilm.
Figure 2 (a) Method developed to segment biofilm from the surface in SEM images. (b) Close-up showing image before and after pre-processing – outliers were removed to smooth scratches on the surface, for better segmentation. (c) Overlay of the segmented area (blue) on the SEM image, showing accurate detection of biofilm.CC BY · PubMed Central
Figure 3 Screenshots of the Trainable Weka Segmentation plugin in Fiji37. (a) Square regions of interest were classified by the user as samples to train the program to segment the rest of the image, where biofilm is labelled green and the surface is labelled magenta. (b) Whole image segmented automatically from the samples given in (a). The Weka logo http://www.cs.waikato.ac.nz/ml/images/Weka (software) logo.png> is available under the Creative Commons Attribution-ShareAlike 2.5 http://creativecommons.org/licenses/by-sa/2.5/ License (http://creativecommons.org/licenses/by-sa/2.5/) for the use of the logo in this figure. Please see the terms of use on the Weka website for more information: http://www.cs.waikato.ac.nz/ml/weka/citing.html.
Figure 3 Screenshots of the Trainable Weka Segmentation plugin in Fiji37. (a) Square regions of interest were classified by the user as samples to train the program to segment the rest of the image, where biofilm is labelled green and the surface is labelled magenta. (b) Whole image segmented automatically from the samples given in (a). The Weka logo http://www.cs.waikato.ac.nz/ml/images/Weka (software) logo.png> is available under the Creative Commons Attribution-ShareAlike 2.5 http://creativecommons.org/licenses/by-sa/2.5/ License (http://creativecommons.org/licenses/by-sa/2.5/) for the use of the logo in this figure. Please see the terms of use on the Weka website for more information: http://www.cs.waikato.ac.nz/ml/weka/citing.html.CC BY · PubMed Central
Figure 4 Examples of comparing the automatic segmentation to a manual reference segmentation. (a) SLA surface with biofilm growth. (b) Overlay in red showing the manual segmentation. (c) Overlay in blue showing the automatic segmentation. (d) Polished surface with biofilm growth. (e) Overlay with manual and (f) automatic segmentation. (g) Receiver Operator Characteristics curve showing accuracy of segmentation for different images.
Figure 4 Examples of comparing the automatic segmentation to a manual reference segmentation. (a) SLA surface with biofilm growth. (b) Overlay in red showing the manual segmentation. (c) Overlay in blue showing the automatic segmentation. (d) Polished surface with biofilm growth. (e) Overlay with manual and (f) automatic segmentation. (g) Receiver Operator Characteristics curve showing accuracy of segmentation for different images.CC BY · PubMed Central
Figure 5 Sensitivity and specificity values obtained when testing the reproducibility of the initial sample pixel choice. All values are over 0.8, showing the method of choosing sample pixels is reproducible.
Figure 5 Sensitivity and specificity values obtained when testing the reproducibility of the initial sample pixel choice. All values are over 0.8, showing the method of choosing sample pixels is reproducible.CC BY · PubMed Central
Figure 6 Examples of SEM images of the polished discs, before and after treatment with the ultrasonic scaler held 1 mm away for 30 s, at low power (a,b) and medium power (c,d). The blue overlay shows the automatic detection of bacteria.
Figure 6 Examples of SEM images of the polished discs, before and after treatment with the ultrasonic scaler held 1 mm away for 30 s, at low power (a,b) and medium power (c,d). The blue overlay shows the automatic detection of bacteria.CC BY · PubMed Central
Figure 7 Examples of SEM images of the SLA discs, before and after treatment with the ultrasonic scaler held 1 mm away for 30 s, at low power (a,b) and medium power (c,d). The blue overlay shows the automatic detection of bacteria.
Figure 7 Examples of SEM images of the SLA discs, before and after treatment with the ultrasonic scaler held 1 mm away for 30 s, at low power (a,b) and medium power (c,d). The blue overlay shows the automatic detection of bacteria.CC BY · PubMed Central
Figure 8 Mean percentage of biofilm remaining calculated from the segmented before and after images, (a) SLA surface, n = 10 for each setting. (b) Polished surface. n = 8 for each setting. *p < 0.05, ***p < 0.001 (Rank Sum Test).
Figure 8 Mean percentage of biofilm remaining calculated from the segmented before and after images, (a) SLA surface, n = 10 for each setting. (b) Polished surface. n = 8 for each setting. *p < 0.05, ***p < 0.001 (Rank Sum Test).CC BY · PubMed Central

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Metadata

Authors
Vyas, N., Sammons, R. L., Addison, O., Dehghani, H., & Walmsley, A. D.
Venue
Scientific Reports
Publisher
Springer Science and Business Media LLC
Published
2016-09-07
Volume
6
Issue
1
ISSN
2045-2322
DOI
10.1038/srep32694
PMC
PMC5013386
Cited by
98
References
0
Language
en
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