Microplastic Identification Using AI-Driven Image Segmentation with Synthetic Ecological Context

Conventional methods for microplastic identification in water samples are costly, slow, and dependent on specialized expertise. We present a deep learning segmentation framework for identifying microplastic foreground in microscopy images and evaluate whether synthetic ecological context can improve model performance when labeled real data are limited. We also contribute a curated image dataset with manually segmented microplastic masks, adding paired image-mask examples for supervised training and held-out ecological evaluation.

The workflow combines manually labeled laboratory microplastic images with generated inpainting examples selected for visible foreground change, non-empty masks, plausible object scale, and background diversity. In our results, adding verified synthetic examples improves segmentation across architectures by increasing exposure to diverse ecological scenarios while preserving pixel-level labels. The strongest synthetic-assisted model reaches Dice 0.817, IoU 0.704, and boundary F1 0.858 on the held-out ecological evaluation set, compared with Dice 0.743, IoU 0.619, and boundary F1 0.809 for the strongest real-only model.