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Updated: Mar 17, 2026

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Automated Neuron Tracing Methods: An Updated Account.

Ludovica Acciai¹, Paolo Soda¹, Giulio Iannello²

¹Department of Engineering, University Campus Bio-Medico of Rome, Roma, Italy.

Neuroinformatics

|July 23, 2016

Summary

This summary is machine-generated.

Automated neuron tracing reconstructs brain cell structures from microscopy images. This survey categorizes and analyzes current and past methods for efficient, accurate neuronal morphology analysis.

Keywords:

BigNeuron Bioimage informatics Digital reconstruction Neuron morphology Neuron tracing Neuroscience

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Laser-guided Neuronal Tracing In Brain Explants

Laser-guided Neuronal Tracing In Brain Explants

Published on: November 25, 2015

Area of Science:

Neuroscience
Computational Biology
Bioimaging

Background:

Investigating brain function relies on understanding neuron morphology.
Manual reconstruction of neuronal structures from microscopy data is impractical due to large datasets.
Automated neuron tracing is essential for analyzing complex neural architectures.

Purpose of the Study:

To survey and contextualize recent advancements in automated neuron tracing methods.
To categorize existing neuron tracing approaches.
To provide an overview of algorithmic components, datasets, and performance metrics.

Main Methods:

Literature review of neuron tracing algorithms.
Categorization into global processing, local processing, and meta-algorithm approaches.
Analysis of algorithmic components, datasets, and performance metrics.

Main Results:

Identification and categorization of various neuron tracing methods.
Overview of the evolution of neuron tracing techniques.
Summary of common datasets and evaluation metrics in the field.

Conclusions:

Automated neuron tracing is critical for modern neuroscience research.
Understanding different algorithmic approaches aids in selecting appropriate methods.
Standardized evaluation is important for comparing neuron tracing performance.