Segmentation and Multimodal Characterization of Metal Parti-cles in the Human Hippocampus

The accumulation of metallic micro and nanoparticles in the human hippocampus is increasingly linked to neurotoxic processes and neurodegenerative disorders. Precise segmentation and detailed characterization of these particles are crucial for understanding their role. This study presents a novel method combining discrete segmentation based on graph-cut theory with Dinic’s algorithm for maximum-flow computation. Images are modelled as directed, weighted graphs, with pixel intensities and gradients defining edge capacities, enabling robust segmentation in electron microscopy data. Morphometric parameters – area, perimeter, circularity, Feret diameters – are automatically extracted. Concurrently, elemental analysis using Energy-Dispersive X-ray Spectroscopy (EDS) reveals a heterogeneous composition, including iron-rich particles and compounds containing nickel and chromium. The observed variability highlights the importance of single-particle analysis to better understand the neurobiological impact of metallic deposits.