<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Cell attachment is of paramount importance in implant design, bioreactor design, tissue engineering and the design of non-fouling surfaces. Surface roughness is a significant factor that affects cell attachment. To explore the impact of roughness characteristics, micromachining approaches can be used to fabricate surfaces with controlled microscale topography. When optical microscopy is employed to study cell attachment to optically opaque micropatterned surfaces, one needs to separate the area of an image coated with cells from the background. Manual cell counting can be used to assess the amount of attached cells. However, this process is very time consuming, when the studied surface is larger than several square millimeters. This paper describes an approach for the automatic estimation of the area of cells attached to the surfaces of micro-patterned optically opaque platforms. <italic>Saccharomyces cerevisiae</italic> yeast cells were used to test the developed approach. The approach uses image registration and segmentation tools available in MathWorks MATLAB R2020b Image Processing Toolbox. The factors that affect the accuracy of the developed approach (magnification, contrast and focus) as well as the ways of improving the results are discussed.</p>
</abstract>

Cell attachment is of paramount importance in implant design, bioreactor design, tissue engineering and the design of non-fouling surfaces. Surface roughness is a significant factor that affects cell attachment. To explore the impact of roughness characteristics, micromachining approaches can be used to fabricate surfaces with controlled microscale topography. When optical microscopy is employed to study cell attachment to optically opaque micropatterned surfaces, one needs to separate the area of an image coated with cells from the background. Manual cell counting can be used to assess the amount of attached cells. However, this process is very time consuming, when the studied surface is larger than several square millimeters. This paper describes an approach for the automatic estimation of the area of cells attached to the surfaces of micro-patterned optically opaque platforms. Saccharomyces cerevisiae yeast cells were used to test the developed approach. The approach uses image registration and segmentation tools available in MathWorks MATLAB R2020b Image Processing Toolbox. The factors that affect the accuracy of the developed approach (magnification, contrast and focus) as well as the ways of improving the results are discussed.

A Matlab-Based Approach for Estimating the Area Taken Up by Cells Attached to Micropatterned Optically Opaque Surfaces

Proceedings of the Latvian Academy of Sciences. Section B. Natural, Exact, and Applied Sciences.

This work is licensed under the Creative Commons Attribution 4.0 International License.

Cell attachment is of paramount importance in implant design, bioreactor design, tissue engineering and the design of non-fouling surfaces. Surface roughness...

A Matlab-Based Approach for Estimating the Area Taken Up by Cells Attached to Micropatterned Optically Opaque Surfaces

Published Online: May 18, 2023

Page range: 109 - 116

Received: Aug 03, 2022

Accepted: Apr 03, 2023

DOI: https://doi.org/10.2478/prolas-2023-0016

Keywords
cell attachment analysis, attached cell area, image processing, MATLAB, micropatterned surfaces

© 2023 Yuri Dekhtyar et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

A Matlab-Based Approach for Estimating the Area Taken Up by Cells Attached to Micropatterned Optically Opaque Surfaces

Published Online: May 18, 2023

Page range: 109 - 116

Received: Aug 03, 2022

Accepted: Apr 03, 2023

DOI: https://doi.org/10.2478/prolas-2023-0016

Keywordscell attachment analysis, attached cell area, image processing, MATLAB, micropatterned surfaces

© 2023 Yuri Dekhtyar et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Keywords
cell attachment analysis, attached cell area, image processing, MATLAB, micropatterned surfaces