Efficient AI-supported quantification of network structures in SEM imaged human mucus

We present an AI-assisted workflow for automated analysis of pore and filament structures in scanning electron microscopy images of mucus from the human respiratory tract and ileum. As a locally executable Fiji plugin, our method enables fast, reproducible, and unbiased measurements. The method outperforms manual analysis in consistency and reliability, reducing labor and variability while increasing throughput substantially. This tool supports high-throughput, comparative studies of biological network structures, improving accuracy and efficiency.