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Characterization of fibronectin networks using graph-based representations of the fibers from 2D confocal images

Anca-Ioana Grapa 1, 2
2 MORPHEME - Morphologie et Images
CRISAM - Inria Sophia Antipolis - Méditerranée , IBV - Institut de Biologie Valrose : U1091, Laboratoire I3S - SIS - Signal, Images et Systèmes
Abstract : A major constituent of the Extracellular Matrix is a large protein called the Fibronectin (FN). Cellular FN is organized in fibrillar networks and can be assembled differently in the presence of two Extra Domains, EDA and EDB. Our objective was to develop numerical quantitative biomarkers to characterize the geometrical organization of the four FN variants (that differ by the inclusion/exclusion of EDA/EDB) from 2D confocal microscopy images, and to compare sane and cancerous tissues. First, we showed through two classification pipelines, based on curvelet features and deep learning framework, that the FN variants can be distinguished with a similar performance to that of a human annotator. We constructed a graph-based representation of the fibers, which were detected using Gabor filters. Graphspecific attributes were employed to classify the variants, proving that the graph representation embeds relevant information from the confocal images. Furthermore, we identified various techniques capable to differentiate the graphs, allowing us to compare the FN variants quantitatively and qualitatively. Performance analysis using toy graphs showed that the methods, which are based on graph matching and optimal transport, can meaningfully compare graphs. Using the graph-matching framework, we proposed different methodologies for defining the prototype graph, representative of a certain FN class. Additionally, the graph matching served as a tool to compute parameter deformation maps between the variants. These deformation maps were analyzed in a statistical framework showing whether or not the variation of the parameters can be explained by the variance within the same class.
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Submitted on : Thursday, December 10, 2020 - 3:01:12 PM
Last modification on : Wednesday, October 27, 2021 - 6:02:15 AM
Long-term archiving on: : Thursday, March 11, 2021 - 7:45:39 PM


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  • HAL Id : tel-03052167, version 1




Anca-Ioana Grapa. Characterization of fibronectin networks using graph-based representations of the fibers from 2D confocal images. Signal and Image processing. Université Côte d'Azur, 2020. English. ⟨NNT : 2020COAZ4031⟩. ⟨tel-03052167⟩



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