A ridge-based detection algorithm with filament overlap identification for 2D mycelium network analysis - Sten, Oscar; Del Dottore, Emanuela; Pugno, Nicola; Mazzolai, Barbara (Ecological informatics, 2024)
Many terrestrial ecosystems engage mycorrhizal symbiotic associations, potentially to enhance nutrition, increase resistance to soil-borne pests and diseases, and improve resilience and soil structure. Mycorrhizal fungi create dynamic networked structures through branching and anastomosis that connect multiple plants and consent to transport resources underground from nutrient-rich patches to demanding plants. Controlled laboratory experiments are fundamental to improving our knowledge of mycelium network growth dynamics and further understanding its role in preserving ecological niches. We propose a method for highly automated analysis of the mycelium network structure and other morphological properties, such as hyphal length, hyphal density, and number of crossing and branches, in 2D microscopy images of fungal samples. Available tools for automated network analyses suffer from overestimating network connectivity since filament crossings are not considered. In particular, we propose a) a ridge-based mycelium detection algorithm and b) a geometrical-based approach to identify overlapping filaments crossing each other. The algorithmic solution is evaluated on a total of 135 real mycelium sample images over different validation steps, originating from different datasets and having different characteristics, including background, contrast, image acquisition system, fungal species, and clearness (e.g., level of transparency, homogeneity, dirtiness of the medium) of the sample. Results show that 1) the proposed detection method can be used to measure the length of mycelium in an image, replacing manual tracing and allowing for less laborious analysis ρ̂c=0.96, 2) the filament detection is on par with state-of-the-art techniques F1=0.88−0.94 with a more intuitive parameterization, and 3) the proposed algorithm correctly identifies filament crossings F1=0.89 in most common cases, yielding a reduction in the overestimation of network connectivity. The latter feature consents to applying the proposed fully automated solution to complex and irregular fungal structures, advancing mycelium detection and reconstruction performance accuracy with respect to the state-of-the-art. •A method for detecting and quantifying mycelium is tested on real fungal images.•A method for identifying filament crossings in the detected mycelium is proposed.•Crossing detection improves estimation of network topology in real fungal images.