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Updated: Aug 23, 2025
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Application of Automated Image-guided Patch Clamp for the Study of Neurons in Brain Slices
Published on: July 31, 2017
11.7K
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.
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.
