IMAGE ANALYSIS OF BRAIN CORTEX CELLS IN VITRO USING A DEEP LEARNING METHOD

Abstract

The article presents a method for analyzing images of cultured cortical cells for a quantitative analysis of the parameters of development of biological neural networks using machine learning approaches. We have developed software modules for segmentation of images into cells, clusters, and neurites using the neural network model and the deep learning method; a training set of images of cultivated neurons and corresponding segmentation masks have been generated. The results were validated by analyzing the development of cultivated neurons in vitro based on the length count of neutrites at different growth stages of the culture. The developed methods for monitoring the processes of formation of biological neuronal networks based on the analysis of the neuronal growth under different conditions and on different substrates provide an opportunity to monitor the processes of stem cell differentiation in the neurogenic direction. The results can be used in monitoring the formation of organoids in bioengineering applications, as well as in modeling the processes of nerve tissue regeneration.