Deep learning for high-throughput quantification of oligodendrocyte ensheathment: a UNet architecture to extract multiple morphological parameters from individual cells
AO_SCPLOWBSTRACTC_SCPLOWHigh-throughput quantification of oligodendrocyte (OL) myelination is a significant challenge that, if addressed, would facilitate the development of therapeutics to promote myelin protection and repair. Here, we established a quantitative high-throughput method to asses OL ensheathment in-vitro, combining nanofiber culture devices and automated imaging with a heuristic approach that informed the development of a deep learning analytic algorithm. The heuristic approach was developed by modeling general characteristics of OL ensheathments, while the deep learning neural network employed a UNet architecture with enhanced capacity to associate ensheathed segments with individual OLs. Reliably extracting multiple morphological parameters from individual cells, without heuristic approximations, mimics the high-level decision-making capacity of human researchers and improves the validity of the neural network. Experimental validation demonstrated that the deep learning approach matched the accuracy of expert-human measurements of the length and number of myelin segments per cell. The combined use of automated imaging and analysis reduces tedious manual labor while eliminating variability. The capacity of this technology to perform multi-parametric analyses at the level of individual cells permits the detection of nuanced cellular differences to accelerate the discovery of new insight into OL physiology.