Segmentation study of nanoparticle topological structures based on synthetic data.

ABSTRACT: Nanoparticles exhibit broad applications in materials mechanics, medicine, energy and other fields. The ordered arrangement of nanoparticles is very important to fully understand their properties and functionalities. However, in materials science, the acquisition of training images requires a large number of professionals and the labor cost is extremely high, so there are usually very few training samples in the field of materials. In this study, a segmentation method of nanoparticle topological structure based on synthetic data (SD) is proposed, which aims to solve the issue of small data in the field of materials. Our findings reveal that the combination of SD generated by rendering software with merely 15% Authentic Data (AD) shows better performance in training deep learning model. The

SUBMITTER: Liang F 

PROVIDER: S-EPMC11446430 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature