Automated noninvasive epithelial cell counting in phase contrast microscopy images with automated parameter selection

Summary Cell counting is commonly used to determine proliferation rates in cell cultures and for adherent cells it is often a ‘destructive’ process requiring disruption of the cell monolayer resulting in the inability to follow cell growth longitudinally. This process is time consuming and utilises significant resource. In this study a relatively inexpensive, rapid and widely applicable phase contrast microscopy‐based technique has been developed that emulates the contrast changes taking place when bright field microscope images of epithelial cell cultures are defocused. Processing of the resulting images produces an image that can be segmented using a global threshold; the number of cells is then deduced from the number of segmented regions and these cell counts can be used to generate growth curves. The parameters of this method were tuned using the discrete mereotopological relations between ground truth and processed images. Cell count accuracy was improved using linear discriminant analysis to identify spurious noise regions for removal. The proposed cell counting technique was validated by comparing the results with a manual count of cells in images, and subsequently applied to generate growth curves for oral keratinocyte cultures supplemented with a range of concentrations of foetal calf serum. The approach developed has broad applicability and utility for researchers with standard laboratory imaging equipment. Lay description The ability to determine the number and growth rates of cells in in vitro cultures of cells has many useful applications, for example in toxicology and drug discovery. Commonly applied methods currently used for counting cells have several disadvantages, including destruction of the cell cultures, large user error and time‐consuming procedures. Phase contrast (PC) microscopy is a widely available type of microscopy that generates contrast in transparent cell cultures without the need for fixation and staining, and allows noninvasive imaging. However, PC image artefacts make it difficult to identify cells easily by means of computational image analysis. This paper describes a method to overcome these artefacts to enable segmentation and counting of cells from PC images. A spatial logic called discrete mereotopology was used to incorporate information regarding the image composition in terms of the tentatively identified cells to find the optimal imaging parameters and maximise the accuracy by removing incorrectly segmented regi