MP-Net: Deep learning-based segmentation for fluorescence microscopy images of microplastics isolated from clams.

ABSTRACT: Environmental monitoring of microplastics (MP) contamination has become an area of great research interest, given potential hazards associated with human ingestion of MP. In this context, determination of MP concentration is essential. However, cheap, rapid, and accurate quantification of MP remains a challenge to this date. This study proposes a deep learning-based image segmentation method that properly distinguishes fluorescent MP from other elements in a given microscopy image. A total of nine different deep learning models, six of which are based on U-Net, were investigated. These models were trained using at least 20,000 patches sampled from 99 fluorescence microscopy images of MP and their corresponding binary masks. MP-Net, which is derived from U-Net, was found to be the best perf

SUBMITTER: Park HM 

PROVIDER: S-EPMC9200300 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature