Sporify: An Automated Tool to Quantify Spores in Z-Stacked 3D Samples

Abstract

AbstractIn recent years, fungi have attracted avid interest from the research community. This interest stems from several motives, including their network creation capabilities and fundamental role in the ecosystem. Controlled laboratory experiments of fungal behaviors are crucial to further understanding their role and functionalities.In this paper, we propose a method for automating the quantification and observation of fungal spores. Our approach consists of four steps: 1) a Z-stack image acquisition of the sample is performed, 2) a detection algorithm is applied to all Z-planes, 3) clustering of spores detected in different Z-planes, 4) determination of the optimal Z-plane for each cluster through an ad-hoc focus measure. We compared the spore count obtained through the automated tool to a manual count and the count obtained by applying the detection algorithm to a single plane. The result is a highly automated, non-invasive tool to determine spore count, estimate each spore depth, and retrieve an all-in-focus image to analyze further.