Automated image-based analysis of spatio-temporal fungal dynamics

Area of Science:

• Mycology
• Computational Biology
• Ecology

Background:

• Fungi are crucial to ecosystems, but studying their growth is challenging due to environmental heterogeneity and experimental limitations.
• Existing image analysis methods for fungal growth often involve manual labor, invasive techniques, or focus on limited aspects of the fungal network.

Purpose of the Study:

• To develop a versatile, automated tool for monitoring fungal growth dynamics in space and time.
• To enable quantitative comparison of fungal growth behaviors across different species and image resolutions.
• To provide a comprehensive analysis of fungal growth features beyond traditional metrics.

Main Methods:

• Integration of image analysis techniques with graph theory for fungal network monitoring.
• Development of a novel experimental setup for functional description of fungal growth dynamics.
• Automated extraction of key fungal growth features including total length, area, fractal dimension, compactness, and morphological parameters.

Main Results:

• The tool successfully monitors fungal growth across various species and image resolutions.
• Automated extraction of diverse growth features (e.g., total length, area, fractal dimension, node degree, growth angle) is achieved.
• The method allows for quantitative comparison of different fungal growth behaviors.

Conclusions:

• The presented automated method offers a comprehensive and versatile alternative to classical mycology approaches.
• This tool facilitates deeper investigation into fungal growth dynamics and morphology.
• Opens new avenues for research in mycology by providing advanced analytical capabilities.