SNT: a unifying toolbox for quantification of neuronal anatomy
By Cameron Arshadi, Ulrik Günther, M. Eddison, Kyle I. S. Harrington, Tiago A. Ferreira. Nature Methods, 2020
396 citations recorded by the source corpus.
SNT is an end-to-end framework for neuronal morphometry and whole-brain connectomics that supports tracing, proof-editing, visualization, quantification and modeling of neuroanatomy. With an open architecture, a large user base, community-based documentation, support for complex imagery and several model organisms, SNT is a flexible resource for the broad neuroscience community. SNT is both a desktop application and multi-language scripting library, and it is available through the Fiji distribution of ImageJ. SNT is a toolbox neuronal morphometry and connectomics and provides various analysis, visualization, quantification and modeling tools.
Subfields: Biophysics, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/f3dab810297ca66792a6d5c5355fa71643c8cede
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Citations in the corpus
- Labkit: Labeling and Segmentation Toolkit for Big Image Data (2021, methodology): “Recently, sparse labeling combined with random forest pixel classification in LABKIT was used to segment mice epidermal cells (Bornes et al., 2021), as well as mRNA foci in neurons (Arshadi et al., 2021).”