Abstract
Automatic image analysis algorithms are in general dedicated quantification tools used for very specific types of microscopic cell images, but are not robust enough to accurately quantify the cell number and distribution in the wide variety for fluorescence images that exist in the field of tissue engineering (TE) today, where cell-material (scaffold) interactions are being evaluated more and more. In this study, a semiautomatic algorithm was developed that allows the user to manually count a limited part of a TE scaffold image, and then automatically counts the cells of the full image based on that calibration dataset. The algorithm was validated on images of cells on a two-dimensional (2D) titanium (Ti) substrate, in a three-dimensional (3D) Ti scaffold and in a fibrin hydrogel by comparison with manual cell counting and with an indirect cell counting using metabolic assay. The average relative error between this semiautomatic and the manual approach was 3.4% for the 2D Ti substrates, 5.9% for the 3D Ti scaffolds, and 14.1% for the fibrin hydrogels. Hereby a proof of concept was delivered that could lead to an increased use of automated cell imaging as a reliable 2D and 3D quantitative tool for both basic biological research and process control of clinical TE products.
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