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Graph‐based unsupervised segmentation algorithm for cultured neuronal networks' structure characterization and modeling
Author(s) -
SantosSierra Daniel,
SendiñaNadal Irene,
Leyva Inmaculada,
Almendral Juan A.,
Ayali Amir,
Anava Sarit,
SánchezÁvila Carmen,
Boccaletti Stefano
Publication year - 2015
Publication title -
cytometry part a
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.316
H-Index - 90
eISSN - 1552-4930
pISSN - 1552-4922
DOI - 10.1002/cyto.a.22591
Subject(s) - segmentation , computer science , artificial intelligence , representation (politics) , algorithm , pattern recognition (psychology) , image segmentation , graph , scaling , theoretical computer science , mathematics , law , politics , political science , geometry
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. © 2014 International Society for Advancement of Cytometry