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Application of Automated Image-guided Patch Clamp for the Study of Neurons in Brain Slices

Application of Automated Image-guided Patch Clamp for the Study of Neurons in Brain Slices

Published on: July 31, 2017

Neuron tracing from light microscopy images: automation, deep learning and bench testing.

Yufeng Liu¹, Gaoyu Wang², Giorgio A Ascoli³

¹School of Biological Science and Medical Engineering, Southeast University, Nanjing, China.

Bioinformatics (Oxford, England)

|October 28, 2022

Summary

This review updates recent progress in automated neuron tracing, covering deep learning methods and large-scale datasets for brain modeling and neuronal analysis.

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Last Updated: Aug 23, 2025

Application of Automated Image-guided Patch Clamp for the Study of Neurons in Brain Slices

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A Rapid Approach to High-Resolution Fluorescence Imaging in Semi-Thick Brain Slices

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Published on: July 26, 2011

Area of Science:

Neuroscience
Computational Biology

Background:

Accurate neuronal morphologies are crucial for understanding brain structure and function.
Automating neuron tracing is vital for large-scale analysis of neuronal data.

Approach:

This review surveys current automatic and semi-automatic neuron tracing methods.
It highlights advancements in deep learning-enhanced techniques for tracing.
The review covers resources, datasets, and benchmarking for neuron tracing.

Key Points:

Deep learning methods are rapidly advancing neuron tracing capabilities.
Large datasets of whole-brain neuron morphologies are now available.
Standardized datasets and metrics are essential for evaluating tracing tools.

Conclusions:

This work provides a comprehensive overview of neuron tracing tools and methods.
It aims to guide researchers in navigating the rapidly evolving field of neuronal morphology analysis.