A Deep Learning Approach for Microplastic Segmentation in Microscopic Images

Abstract

The ubiquitous presence of microplastics across environmental compartments presents a formidable ecotoxicological and risk assessment challenge, fundamentally complicated by the link between microplastic morphology and differential toxicological outcomes. Current analytical methods face a significant measurement bottleneck, hindering the precise, high-throughput characterization needed for robust mechanistic and exposure studies. To address this, we introduce MNv4-Conv-M-fpn, a novel deep learning model specifically engineered for multi-class microplastic segmentation and morphological characterization from microscopic images. This model is designed to provide the toxicologically-relevant granularity required for rigorous risk assessment, segmenting images into six classes: five distinct microplastic categories (fiber, fragment, sphere, foam, and film) and the background. By incorporating advanced architectural features—including transfer learning, a Feature Pyramid Network, and a Feature Fusion Module—our approach achieves high accuracy, computational efficiency, and near real-time inference speed. Comprehensive validation using a diverse dataset demonstrates that MNv4-Conv-M-fpn outperforms existing segmentation methods while maintaining low computational load. This makes the model well-suited for high-throughput deployment in environmental laboratories and resource-constrained monitoring efforts. This approach offers a valuable tool for environmental monitoring, enabling more precise and scalable analysis of microplastic pollution in various ecosystems.