Nature Communications (May 2024)

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,
  • Nicola Vanello

DOI
https://doi.org/10.1038/s41467-024-48146-y
Journal volume & issue
Vol. 15, no. 1
pp. 1 – 17

Abstract

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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.