Etude de la morphologie et de la distribution des neurones dans le cerveau de macaque par microscopie optique

Abstract : Understanding the mechanisms involved in healthy cases, neurodegenerative diseases and the development of new therapeutic approaches is based on the use of relevant experimental models and appropriate imaging techniques. In this context, virtual microscopy offers the unique possibility of analyzing these models at a cellular scale with a very wide variety of histological markers. My thesis project consists in carrying out and applying a method of analyzing colored histological images that can segment and synthesize information corresponding to neurons using the NeuN antibody on sections of the macaque brain. In this work, we first apply the Random Forest (RF) method to segment neurons as well as tissue and background. Then, we propose an original method to separate the touching or overlapping neurons in order to individualize them. This method is adapted to process neurons presenting a variable size (diameter varying between 5 and 30 μm). It is also effective not only for so-called "simple" regions characterized by a low density of neurons but also for so-called "complex" regions characterized by a very high density of several thousands of neurons. The next work focuses on the creation of parametric maps synthesizing the morphology and distribution of individualized neurons. For this purpose, a multiscale approach is implemented in order to produce maps with lower spatial resolutions (0.22 μm original resolution and created maps offering adaptive spatial resolution from a few dozens to a few hundred of micrometers). Several dozens of morphological parameters (mean radius, surface, orientation, etc.) are first computed as well as colorimetric parameters. Then, it is possible to synthesize this information in the form of lower-resolution parametric maps at the level of anatomical regions, sections and even, eventually, the entire brains. This step transforms qualitative color microscopic images to quantitative mesoscopic images, more informative and easier to analyze. This work makes it possible to statistically analyze very large volumes of data, to synthesize information in the form of quantitative maps, to analyze extremely complex problems such as neuronal death, to test new drugs and to compare this acquired information post mortem with data acquired in vivo.
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Zhenzhen You. Etude de la morphologie et de la distribution des neurones dans le cerveau de macaque par microscopie optique. Bio-informatique [q-bio.QM]. Université Pierre et Marie Curie - Paris VI, 2017. Français. ⟨NNT : 2017PA066278⟩. ⟨tel-02311480⟩

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