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

Traditional methods to quantify mycelial growth rely on destructive sampling to quantify biomass. Moreover, these approaches limit continuous observation and require a sufficient mass to measure. Recent work examines hyphal network traits by reconstructing the hyphal network from spatial coordinates via images, providing information about branching patterns and spatial growth over time.

We developed SkelPy, a Python-based graphical user interface that skeletonizes images of hyphal networks and extracts biologically relevant structural parameters such as fractal dimension, a proxy for the complexity and branching structure of the hyphal network. Using a high-throughput pipeline, we imaged three isolates of Botrytis cinerea grown in liquid culture for 72 h, generating a dataset of 180 time-series images.

SkelPy enables efficient, non-destructive, and scalable quantification of hyphal growth and complexity from time-resolved image datasets, providing a powerful and user-friendly tool for studying fungal network dynamics.