Image Processing Methods for the Automated Assessmentof Neuronal Outgrowth

Neuronal outgrowth assessment is useful to understand the development of peripheral or central neurons and their regeneration after wounding. It consists in the determination of the length of the cell extensions (neurite length) using photos of neuron cultures. As the manual determination of neurite length is time-consuming and operator-dependent, many semior fully-automated methods have been developed. Most of them have been designed to analyze fluorescence microscopy images which allow clear delineation of cell bodies and neurites from the background. In this paper, we propose a new easy-to-use fully automated computer vision methodbased on denoising, background subtraction, edge and envelope detection, and designed to analyze compressed images (JPEG format) of non-fluorescent living neurons. A statistical tool was also integrated in the program to provide turnkey data to biologists. The reliability of our program was tested using images of differentiated PC-12 cell culture. Statistical analysis showed non-significant difference between the manual determination and our automated method.

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