Automated neurite labeling and analysis in fluorescence microscopy images

Area of Science:

• Neuroscience
• Computational Biology
• Image Analysis

Background:

• Accurate and reproducible labeling and measurement of neurites are crucial for understanding intricate nervous processes via computerized image analysis.
• Existing manual methods for neurite analysis are labor-intensive and can lack reproducibility.
• There is a need for automated tools to facilitate large-scale neurite analysis in biological research.

Purpose of the Study:

• To develop and validate an automated method for neurite analysis.
• To enable accurate and efficient extraction and measurement of neurite features, including centerlines, branching, and endpoints.
• To assess the performance of the automated method against manual analysis.

Main Methods:

• An automated neurite analysis approach was developed, incorporating user interaction for initial parameter setting.
• The method extracts single and connected centerlines along neurites, identifying branching and end points.
• Multi-scale flexibility allows for simultaneous detection of both thick and thin neurites.

Main Results:

• The automated method demonstrated high accuracy, with an average relative neurite length difference of approximately 0.02 and an average centerline deviation of 2.8 pixels.
• Kolmogorov-Smirnov (KS) tests showed a 99.79% probability of the distributions of automated and manual results being the same.
• The KS test confirmed no significant bias between different observers using the new validation scheme.

Conclusions:

• The proposed automated neurite analysis method provides accurate extraction of neurite centerlines and measurement of neurite lengths.
• This method significantly reduces human labor and enhances efficiency compared to manual tracing.
• The tool is suitable for large-scale neurite analysis, exceeding the capabilities of manual methods.