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Journal Articles Nature Communications Year : 2024

A modular framework for multi-scale tissue imaging and neuronal segmentation

Abstract

Abstract The development of robust tools for segmenting cellular and sub-cellular neuronal structures lags behind the massive production of high-resolution 3D images of neurons in brain tissue. The challenges are principally related to high neuronal density and low signal-to-noise characteristics in thick samples, as well as the heterogeneity of data acquired with different imaging methods. To address this issue, we design a framework which includes sample preparation for high resolution imaging and image analysis. Specifically, we set up a method for labeling thick samples and develop SENPAI, a scalable algorithm for segmenting neurons at cellular and sub-cellular scales in conventional and super-resolution STimulated Emission Depletion (STED) microscopy images of brain tissues. Further, we propose a validation paradigm for testing segmentation performance when a manual ground-truth may not exhaustively describe neuronal arborization. We show that SENPAI provides accurate multi-scale segmentation, from entire neurons down to spines, outperforming state-of-the-art tools. The framework will empower image processing of complex neuronal circuitries.
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inserm-04604576 , version 1 (07-06-2024)

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Simone Cauzzo, Ester Bruno, David Boulet, Paul Nazac, Miriam Basile, et al.. A modular framework for multi-scale tissue imaging and neuronal segmentation. Nature Communications, 2024, 15 (1), pp.4102. ⟨10.1038/s41467-024-48146-y⟩. ⟨inserm-04604576⟩
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