http://www.cnr.it/ontology/cnr/individuo/prodotto/ID289394

Graph-based unsupervised segmentation algorithm for cultured neuronal networks' structure characterization and modeling (Articolo in rivista)

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Graph-based unsupervised segmentation algorithm for cultured neuronal networks' structure characterization and modeling (Articolo in rivista) (literal)

Anno

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Alternative label

Daniel de Santos-Sierra (1,2); Irene Sendiña-Nadal (1,3); Inmaculada Leyva (1,3); Juan A. Almendral (1,3); Amir Ayali (4); Sarit Anava (4,5); Carmen Sánchez-Ávila (2); Stefano Boccaletti (6,7) (2014)
Graph-based unsupervised segmentation algorithm for cultured neuronal networks' structure characterization and modeling
in Cytometry. Part A (Online); International Society for Advancement of Cytometry, Bethesda, Maryland (Stati Uniti d'America)
(literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori

Daniel de Santos-Sierra (1,2); Irene Sendiña-Nadal (1,3); Inmaculada Leyva (1,3); Juan A. Almendral (1,3); Amir Ayali (4); Sarit Anava (4,5); Carmen Sánchez-Ávila (2); Stefano Boccaletti (6,7) (literal)

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(1) Centre for Biomedical Technology, Universidad Politécnica de Madrid, Madrid, Spain (2) Group of Biometrics, Biosignals and Security, Universidad Politécnica de Madrid, Madrid, Spain (3) Complex Systems Group, Universidad Rey Juan Carlos, Madrid, Spain (4) Department of Zoology, Tel Aviv University, Tel Aviv, Israel (5) Department of Neurobiology, Wise Faculty of Life Sciences & Sagol School, Tel Aviv University, Tel Aviv, Israel (6) Embassy of Italy in Tel Aviv, Tel Aviv, Israel (7) CNR-Istituto dei Sistemi Complessi, Florence, Italy (literal)

Titolo

Graph-based unsupervised segmentation algorithm for cultured neuronal networks' structure characterization and modeling (literal)

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. © 2014 International Society for Advancement of Cytometry (literal)

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