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. 2015 Jun;87(6):513-23.
doi: 10.1002/cyto.a.22591. Epub 2014 Nov 12.

Graph-based Unsupervised Segmentation Algorithm for Cultured Neuronal Networks' Structure Characterization and Modeling

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Graph-based Unsupervised Segmentation Algorithm for Cultured Neuronal Networks' Structure Characterization and Modeling

Daniel de Santos-Sierra et al. Cytometry A. .
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Abstract

Large scale phase-contrast images taken at high resolution through the life of a cultured neuronal network are analyzed by a graph-based unsupervised segmentation algorithm with a very low computational cost, scaling linearly with the image size. The processing automatically retrieves the whole network structure, an object whose mathematical representation is a matrix in which nodes are identified neurons or neurons' clusters, and links are the reconstructed connections between them. The algorithm is also able to extract any other relevant morphological information characterizing neurons and neurites. More importantly, and at variance with other segmentation methods that require fluorescence imaging from immunocytochemistry techniques, our non invasive measures entitle us to perform a longitudinal analysis during the maturation of a single culture. Such an analysis furnishes the way of individuating the main physical processes underlying the self-organization of the neurons' ensemble into a complex network, and drives the formulation of a phenomenological model yet able to describe qualitatively the overall scenario observed during the culture growth.

Keywords: automated tracing; complex networks; connectome reconstruction; cultured neuronal network; high throughput; light microscopy; network topology analysis; neurite tracing; neuron image segmentation; neuronal morphology.

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