The study of the electrophysiological properties of neurons has reached a new level thanks to recent techniques that combine knowledge from different fields of science. For a method such as all-optical electrophysiology, the quality of cell segmentation in the image has one of the critical roles since the accuracy of illumination and perturbation of cells depends on it. The task becomes challenging because neurons have a complex morphology, and therefore traditional image analysis methods cannot perform accurate segmentation.
This project focuses on building two AI-based models for neuron soma detection and mask prediction, as well as such an essential aspect of the experiment as the quality of the image recordings. Developed models demonstrate high performance and are ready to be applied to the images of cells with or without fluorescent labels, although expanding the training dataset is recommended for improving segmentation accuracy. In addition, the signal-to-noise ratio was measured for recordings with different parameters such as camera readout speed, illumination intensity, and frequency of laser switching. The project can be extended to detect and segment dendritic trees and spines to gain new insights into the subtle process of intercellular communication.