Automated Facet and Volume Segmentation of Nanoparticles in Simulated CTEM Images Using Deep Learning

Nanoparticles are important in materials science and engineering, with applications in biomedicine and electronics. Understanding their physical and structural properties relies on electron microscopy imaging techniques. Traditionally, these images have been analyzed manually, which is costly, requires specialized expertise, and limits throughput. Recently, artificial intelligence, particularly deep learning, has emerged as a powerful alternative for image analysis, enabling automated detection and segmentation. However, most existing models are designed for general-purpose tasks, and few studies focus on nanoparticle segmentation in electron microscopy images. Moreover, implementing such models often requires significant computational resources and large annotated datasets, which may not be accessible in all research settings. This study evaluates deep-learning segmentation models for identifying facets and volumes in simulated images of gold nanoparticles. Using transfer learning, we assess YOLOv11n-seg as a lightweight instance-segmentation model for facet and volume segmentation in simulated CTEM images of gold decahedral and icosahedral nanoparticles. Its use was motivated by its compact architecture within the YOLO segmentation family and its potential for efficient inference relative to larger YOLO segmentation variants. Because only simulated CTEM data are used, the reported performance should be interpreted as simulation-domain performance; validation on experimental micrographs remains necessary before practical deployment.