Direct Cell Counting Using Macro-Scale Smartphone Images of Cell Aggregates

Abstract

The field of bioengineering depends on technologies for stable cell culture. Conventionally, every process involved in cell culture has been performed manually, so the culture efficiency and stability can vary between trials or depending on the technician. Among these processes, cell counting is particularly important because cell density affects cell function. Conventional cell counting techniques for cell number estimation are inefficient and unstable because they involve the manual work of collecting a sample of the cell suspension. Thus, a cell counting method that is not susceptible to human error is needed. In this study, we present a novel cell counting method based on smartphone imaging and convolutional neural network-based image processing. Cells are aggregated by centrifuging in a tube and then imaged using a smartphone. The image is transferred to a server, and the cell number is predicted using convolutional neural networks on the server. All processes are performed by a custom-developed smartphone-compatible web app. Compared with the conventional method using a hemocytometer, our method yields more stable cell counting. Furthermore, the time and labor required for cell counting are significantly reduced. Our new method could potentially replace conventional cell counting techniques and thus enhance the stability and efficiency of bioengineering studies that require cell culture.

Keywords

• Cell counting
• cell culture process
• regression analysis
• supervised learning
• computational biology
• machine learning