Automated Cell Viability Analysis in Tissue Scaffolds

Abstract

Image processing techniques are frequently used for extracting quantitative information (cell area, cell size, cell counting, etc.) from different types of microscopic images. Image analysis of cell biology and tissue engineering is time consuming and requires personal expertise. In addition, evaluation of the results may be subjective. Therefore, computer-based learning applications have been rapidly developed in recent years. In this study, Confocal Laser Scanning Microscope (CLSM) images of the viable pre-osteoblastic mouse MC3T3-E1 cells in 3D bioprinted tissue scaffolds, captured from a bone tissue regeneration study, were analyzed by using image processing techniques. The goal of this study is to develop a reliable and fast algorithm for semi-automatic analysis of CLSM images. Percentages of live and dead cell areas in the scaffolds were determined with image correlation, and then, total cell viabilities were calculated. The other goal of this study is to determine the depth profile of viable cells in 3D tissue scaffold. Manual measurements of four different analysts were obtained. The measurement variations of analysts, also known as the coefficient of variation, were determined from 13.18% to 98.34% for live cell images and from 9.75% to 126.02% for dead cell images. To overcome this subjectivity, a semi-automatic algorithm was developed. Consequently, cross-sectional image sets of three different types of tissue scaffolds were analyzed. As a result, maximum cell viabilities were obtained at intervals of 63 µm and 90 µm from the scaffold surface.

Keywords

• image correlation
• edge detection
• cell area counting
• tissue engineering

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