SkelPy: A graphic user interface-based approach for skeletonizing fungal networks.

Area of Science:

• Mycology
• Computational Biology
• Image Analysis

Background:

• Traditional methods for quantifying mycelial growth involve destructive sampling, limiting continuous observation.
• Existing approaches struggle to capture the intricate details of hyphal network structure and dynamics over time.

Purpose of the Study:

• To develop a novel, non-destructive method for quantifying fungal mycelial growth and complexity.
• To provide a user-friendly tool for analyzing hyphal network traits from image data.

Main Methods:

• Development of SkelPy, a Python-based graphical user interface for skeletonizing fungal hyphal network images.
• Extraction of biologically relevant structural parameters, including fractal dimension, to assess network complexity.
• Application of a high-throughput pipeline to image three isolates of *Botrytis cinerea* over 72 hours, generating 180 time-series images.

Main Results:

• SkelPy successfully skeletonizes hyphal networks and extracts key structural parameters.
• The software enables efficient and scalable quantification of fungal growth from time-resolved image datasets.
• Analysis of *Botrytis cinerea* isolates provided insights into their hyphal network dynamics.

Conclusions:

• SkelPy provides an efficient, non-destructive, and scalable solution for quantifying hyphal growth and complexity.
• The tool facilitates the study of fungal network dynamics using time-resolved image data.
• SkelPy represents a powerful and user-friendly advancement for mycological research.