Creating High-Resolution Microscopic Cross-Section Images of Hardwood Species Using Generative Adversarial Networks.

ABSTRACT: Microscopic wood identification plays a critical role in many economically important areas in wood science. Historically, producing and curating relevant and representative microscopic cross-section images of wood species is limited to highly experienced and trained anatomists. This manuscript demonstrates the feasibility of generating synthetic microscopic cross-sections of hardwood species. We leveraged a publicly available dataset of 119 hardwood species to train a style-based generative adversarial network (GAN). The proposed GAN generated anatomically accurate cross-section images with remarkable fidelity to actual data. Quantitative metrics corroborated the capacity of the generative model in capturing complex wood structure by resulting in a Fréchet inception distance score of 17.38

SUBMITTER: Lopes DJV 

PROVIDER: S-EPMC8548738 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature