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Descripción
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| This thesis analyzes the morphological evolution of assemblies of living neurons, as they self-organize from collections of separated cells into elaborated, clustered, networks. In particular, the contribution consists in the design and implementation of a graph-based unsupervised segmentation algorithm with a very low computational cost. The processing automatically retrieves the whole network structure from large scale phase-contrast images taken at high resolution throughout the entire life of a cultured neuronal network. The network structure is represented by a mathematical object (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 all 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 measures are non invasive and entitle us to carry out a fully longitudinal analysis during the maturation of a single culture. | |
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Internacional
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No |
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ISBN
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Tipo de Tesis
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Doctoral |
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Calificación
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Sobresaliente cum laude |
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Fecha
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27/04/2015 |