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Developing a Segmentation Model for Microscopic Images of Microplastics Isolated from Clams
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Researchers developed a deep learning segmentation model to automatically identify and quantify microplastics in microscopic images extracted from clams, offering a faster and more consistent alternative to manual counting for assessing MP contamination in seafood.
Microplastics (MP) have become a major concern, given the threat they pose to marine-derived food and human health. One way to investigate this threat is to quantify MP found in marine organisms, for instance making use of image analysis to identify ingested MP in...
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Defining Irregular Microplastics: A Machine Learning Approach for Morphometric Characterization
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Researchers applied a machine learning decision-tree classifier to hyperspectral images of 129 nano- and microplastic particles and identified five morphometric descriptors—circularity, roundness, solidity, long diameter, and perimeter-area ratio—that together distinguish irregular particles from spherical and fibrous forms with 96% accuracy, providing a standardized definition for the most environmentally prevalent but least-defined shape class.
MicroDetect-Net (MDN): Leveraging Deep Learning to Detect Microplastics in Clam Blood, a Step Towards Human Blood Analysis
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Researchers developed MicroDetect-Net, a deep learning model trained to detect microplastics in clam blood, demonstrating a pathway toward automated detection in human blood samples as plastic production exceeds 368 million tons annually and microplastics are found across all environmental and biological matrices.
Proceeding the categorization of microplastics through deep learning-based image segmentation
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Researchers developed a deep learning-based image segmentation method using Mask R-CNN to automatically identify and classify microplastic shapes in microscopic images, demonstrating a practical step toward standardized and automated microplastic categorization.
Advanced Microplastic Identification in Marine Environments via Hybrid Deep Learning
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Researchers propose a hybrid deep learning architecture combining 3D convolutional neural networks and Vision Transformers applied to hyperspectral imagery to detect and classify microplastics in turbid marine environments, capturing both local spectral signatures and global contextual patterns that single-model approaches miss.
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When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.