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Updated: May 14, 2026

Two Algorithms for High-throughput and Multi-parametric Quantification of Drosophila Neuromuscular Junction Morphology
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Two Algorithms for High-throughput and Multi-parametric Quantification of Drosophila Neuromuscular Junction Morphology

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NEuronMOrphological analysis tool: open-source software for quantitative morphometrics.

Lucia Billeci1, Chiara Magliaro, Giovanni Pioggia

  • 1Institute of Clinical Physiology (IFC), National Research Council of Italy (CNR) Pisa, Italy.

Frontiers in Neuroinformatics
|February 20, 2013
PubMed
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This study introduces NEMO, a novel software tool for automated neuron morphometric analysis. NEMO efficiently processes large image datasets, aiding research in brain development and disease.

Area of Science:

  • Neuroscience
  • Computational Biology
  • Biomedical Imaging

Background:

  • Neuron morphometric analysis is crucial for understanding brain development and neurodegenerative diseases.
  • Advancements in neural imaging generate complex, large-scale datasets.
  • Existing tools may not adequately handle high-throughput analysis of neuron morphology.

Purpose of the Study:

  • To introduce NEMO (NEuronMOrphological analysis tool), a software designed for automated batch processing of neuron images.
  • To enable efficient data handling, storage, and multivariate classification of neuronal morphology.
  • To compare NEMO's capabilities with existing image processing tools.

Main Methods:

  • Development of NEMO software for automated analysis of optical microscopy images of neurons.
Keywords:
3-way PCAimage processingmorphometricsneuronssoftware

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Last Updated: May 14, 2026

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12:29

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Published on: May 3, 2017

Automated Sholl Analysis of Digitized Neuronal Morphology at Multiple Scales
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Automated Sholl Analysis of Digitized Neuronal Morphology at Multiple Scales

Published on: November 14, 2010

  • Implementation of batch processing routines for large image datasets.
  • Application of 3-way principal component analysis (PCA) for feature extraction and classification.

    • Main Results:

    • NEMO successfully automates the analysis of large numbers of neuron images.
    • The software facilitates multivariate classification and feature extraction.
    • Demonstrated utility of NEMO in classifying neurons from wild-type and autism model mice.

    Conclusions:

    • NEMO provides an efficient solution for high-throughput neuron morphometric analysis.
    • The tool aids in distinguishing neuronal subtypes and disease models.
    • NEMO enhances research capabilities in neurodevelopmental and neurodegenerative studies.