A deep learning-based framework for automatic analysis of the nanoparticle morphology in SEM/TEM images

Abstract

Scanning electron microscopy (SEM) and transmission electron microscopy (TEM) are important tools for characterizing nanomaterial morphology. Automatic analysis of the nanomaterial morphology in SEM/TEM images plays a crucial role in accelerating research on nanomaterials science. However, achieving a high-throughput automated online statistical analysis of the nanomaterial morphology in various complex SEM/TEM images is still a challenging task. In this paper, we propose a universal framework based on deep learning to perform a fast and accurate online statistical analysis of the nanoparticle morphology in complex SEM/TEM images. The proposed framework consists of three stages that are nanoparticle segmentation using a powerful light-weight deep learning network (NSNet), nanoparticle shape extraction, and statistical analysis. The experimental results show that NSNet in the proposed framework has achieved an accuracy of 86.2% and can process 11 SEM/TEM images per second on an embedded processor. Compared with other semantic segmentation models, NSNet is an optimal choice to ensure that the proposed framework still achieves accurate and fast segmentation even in SEM/TEM images with high background interference, extremely small nanoparticles and dense nanoparticles. Meanwhile, the equivalent diameter and Blaschke shape coefficient of the nanoparticle obtained by the proposed framework are 17.14 ± 5.9 and 0.18 ± 0.04, which are well consistent with those of manual statistical analysis. In short, the proposed framework has a promising future in driving the development of automatic and intelligent analysis technology for nanomaterial morphology.