Cytopathic Effect Detection and Clonal Selection using Deep Learning.

Abstract

Purpose: In biotechnology, microscopic cell imaging is often used to identify and analyze cell morphology and cell state for a variety of applications. For example, microscopy can be used to detect the presence of cytopathic effects (CPE) in cell culture samples to determine virus contamination. Another application of microscopy is to verify clonality during cell line development. Conventionally, inspection of these microscopy images is performed manually by human analysts. This is both tedious and time consuming. In this paper, we propose using supervised deep learning algorithms to automate the cell detection processes mentioned above.

Methods: The proposed algorithms utilize image processing techniques and convolutional neural networks (CNN) to detect the presence of CPE and to verify the clonality in cell line development.

Results: We train and test the algorithms on image data which have been collected and labeled by domain experts. Our experiments have shown promising results in terms of both accuracy and speed.

Conclusion: Deep learning algorithms achieve high accuracy (more than 95%) on both CPE detection and clonal selection applications, resulting in a highly efficient and cost-effective automation process.