Deep learning for automated size and shape analysis of nanoparticles in scanning electron microscopy

Abstract

The automated analysis of nanoparticles, imaged by scanning electron microscopy, was implemented by a deep-learning (artificial intelligence) procedure based on convolutional neural networks (CNNs). It is possible to extract quantitative information on particle size distributions and particle shapes from pseudo-three-dimensional secondary electron micrographs (SE) as well as from two-dimensional scanning transmission electron micrographs (STEM). After separation of particles from the background (segmentation), the particles were cut out from the image to be classified by their shape (e.g. sphere or cube). The segmentation ability of STEM images was considerably enhanced by introducing distance- and intensity-based pixel weight loss maps. This forced the neural network to put emphasis on areas which separate adjacent particles. Partially covered particles were recognized by training and excluded from the analysis. The separation of overlapping particles, quality control procedures to exclude agglomerates, and the computation of quantitative particle size distribution data (equivalent particle diameter, Feret diameter, circularity) were included into the routine.