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

Simone Cauzzo (), Ester Bruno, David Boulet, Paul Nazac, Miriam Basile, Alejandro Luis Callara, Federico Tozzi, Arti Ahluwalia, Chiara Magliaro, Lydia Danglot () and Nicola Vanello ()
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Simone Cauzzo: University of Pisa
Ester Bruno: University of Pisa
David Boulet: NeurImag Core Facility
Paul Nazac: Membrane traffic and diseased brain
Miriam Basile: University of Pisa
Alejandro Luis Callara: University of Pisa
Federico Tozzi: University of Pisa
Arti Ahluwalia: University of Pisa
Chiara Magliaro: University of Pisa
Lydia Danglot: NeurImag Core Facility
Nicola Vanello: University of Pisa

Nature Communications, 2024, vol. 15, issue 1, 1-17

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.

Date: 2024
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DOI: 10.1038/s41467-024-48146-y

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