engleski

Machine learning-based image analysis of optically detected neurons cultured in-vitro on high-density micro-pillar substrates and chips

We present the novel method for machine learning- based image processing of optically detected neurons, grown as in-vitro cultures on top of the patterned high-density micro-pillar substrates and chips. We first validated the method with the control images of in-vitro cultures of dorsal root ganglion neurons grown on glass coverslips. Then, we showed that the method is able to reveal morphological differences between neonatal and adult dorsal root ganglion neurons, as well as the estimation of their in-vitro age. The method was optimized for fast image analysis of large image datasets of neurons.

Fluorescence microscopy ; Image processing ; Machine learning ; Neurons

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