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Papers
20 resultsShowing papers similar to Using digital pathology to standardize and automate histological evaluations of environmental samples
ClearImage processing tools in the study of environmental contamination by microplastics: reliability and perspectives
Researchers assessed the reliability of image processing tools for studying microplastic contamination, finding that while these tools offer efficiency gains, inconsistent methodologies limit comparability between studies and call for standardization.
Identification Tools of Microplastics from Surface Water Integrating Digital Image Processing and Statistical Techniques
This study demonstrated that digital image analysis can automate and improve the characterization of microplastic particles collected from river water, capturing detailed shape, color, and size data that manual microscopy cannot easily achieve at scale. Better identification tools like this are essential for standardizing microplastic monitoring across different waterways and research groups.
Digital Image Identification of Plankton Using Regionprops and Bagging Decision Tree Algorithm
Researchers developed a digital image classification system using machine learning to identify and count plankton from microscopy images. The method reduced the time and subjectivity of manual identification while maintaining accuracy. Automated plankton identification could also be adapted to distinguish microplastics from biological particles in environmental water samples.
A highly accurate and semi-automated method for quantifying spherical microplastics based on digital slide scanners and image processing
Researchers developed a semi-automated image analysis system — combining a digital slide scanner with custom software — that can count and size spherical microplastics in water samples with less than 0.6% error, down to a minimum particle size of 1 micrometer. The system outperforms manual counting in speed and consistency, and was validated in both clean and polluted water. Accurate, high-throughput quantification tools like this are essential for producing reliable microplastic data that can be compared across laboratories and used to set health and environmental standards.
A Machine Learning Approach To Microplastic Detection And Quantification In Aquatic Environments
This study developed a machine learning approach for detecting and quantifying microplastics in aquatic environments, demonstrating that automated image analysis can improve throughput and accuracy compared to manual microscopic counting for environmental monitoring applications.
Digital holographic microplastics detection and characterization in heterogeneous samples via deep learning
Researchers used digital holographic microscopy combined with deep learning to detect and characterize microplastic particles in heterogeneous samples containing algae, microorganisms, and other natural particles. This automated approach could improve the speed and accuracy of environmental microplastic monitoring.
Improvements in histological technique for the ecotoxicological assessment using small biological samples
Researchers improved histological techniques for ecotoxicological assessment of small biological samples, refining tissue processing and staining protocols to better characterize cellular morphology and physiology in small test organisms commonly used in bioassays.
A new approach for routine quantification of microplastics using Nile Red and automated software (MP-VAT)
Researchers developed a new workflow combining Nile Red fluorescence staining with automated image analysis software (MP-VAT) to rapidly quantify microplastics in environmental samples, reducing the labor and subjectivity of manual counting methods. The automated approach improves throughput and reproducibility for routine microplastic monitoring applications.
Application of Pattern Recognition and Computer Vision Tools to Improve the Morphological Analysis of Microplastic Items in Biological Samples
Researchers developed and validated an open-source image analysis procedure for measuring morphological characteristics of microplastic items identified in fish organ samples, using manually set edge points in digital microscope images and comparison against commercial MotiConnect software. The proposed workflow enabled accurate calculation of shape descriptors such as length, width, and item area, offering a cost-effective alternative for routine laboratory microplastic morphological analysis.
Imaging and spectroscopic analysis of pathogens in water, and their classification with machine learning algorithms
Researchers developed an integrated approach for automated classification of cyanobacterial pathogens in water using dark-field illumination imaging combined with Raman spectroscopy, with machine learning algorithms applied for rapid species identification. The system aims to reduce pathogen detection times in water quality monitoring compared to conventional culture-based methods.
A Critical Review on Artificial Intelligence—Based Microplastics Imaging Technology: Recent Advances, Hot-Spots and Challenges
Researchers reviewed the use of artificial intelligence and machine learning techniques for detecting and identifying microplastics in environmental samples. The study found that AI-based imaging tools can significantly speed up analysis and improve accuracy compared to traditional manual methods. However, challenges remain around standardizing datasets and making these tools accessible for routine environmental monitoring.
Detection of Microplastics Using Machine Learning
Researchers reviewed and demonstrated machine learning approaches for detecting and classifying microplastics in environmental samples, finding that automated image analysis and spectral classification methods can improve the speed and accuracy of microplastic monitoring compared to manual methods.
Detection and identification of environmental faunal proxies in digital images and video footage from northern Norwegian fjords and coastal waters using deep learning object detection algorithms
Researchers developed deep learning object detection algorithms to automate the detection and identification of environmental faunal proxies in digital images and video footage from Norwegian fjords and coastal waters, as part of the ICT+ ocean surveying project at UiT The Arctic University of Norway. The preliminary work aimed to automate identification of objects ranging from foraminifera and microplastics at the micrometre scale to boulders and shipwrecks at the metre scale, replacing labour-intensive manual processing.
Development of a toolbox for the analysis of microplastic-tissue interactions in two benthic freshwater organisms
Researchers developed adapted histological protocols for analyzing how microplastic particles interact with tissues in two freshwater invertebrate species. Standard histological methods often use solvents that dissolve plastics, making them incompatible with microplastic studies, so the team modified existing techniques to preserve plastic particles within tissue samples. The resulting toolbox enables researchers to determine whether ingested microplastics simply pass through the gut or actually translocate into organism tissues.
Microplastics quantification in sewage sludge: A rapid and cost-effective approach
Researchers developed a rapid and cost-effective image-based method for quantifying microplastics in sewage sludge, using digital image analysis to count and size MP particles without requiring expensive spectroscopic equipment, offering a practical tool for routine sludge monitoring.
Plankton classification with high-throughput submersible holographic microscopy and transfer learning
Researchers used underwater holographic microscopes and transfer learning — an AI technique that applies knowledge from one task to another — to automatically classify diverse plankton species from images, including rare forms. The system shows promise for large-scale, automated ocean monitoring without needing constant human analysis.
Leveraging AI tools for microplastic data quality assessment
Researchers explored how AI tools can improve data quality assessment in microplastic studies, which vary widely in methodological rigor. The approach aims to standardize quality evaluation so that human health risk assessments based on microplastic research are more reliable.
A Deep Learning Approach for Microplastic Segmentation in Microscopic Images
Researchers developed a deep learning model for automated segmentation and classification of microplastics in microscopic images, identifying five distinct categories including fibers, fragments, spheres, foam, and film. The model achieved high accuracy while maintaining low computational requirements, making it suitable for high-throughput deployment in environmental monitoring. The study offers a tool that could help overcome the measurement bottleneck in microplastic characterization for toxicological and risk assessment studies.
Deep Learning-Based Image Recognition System for Automated Microplastic Detection and Water Pollution Monitoring
This study developed a deep learning image recognition system to automate the detection and classification of microplastics from microscopy images of water samples. The system achieved high accuracy across particle types and sizes, offering a scalable and less labor-intensive alternative to manual microscopy for large-scale water pollution monitoring.
Development of a toolbox for the analysis of microplastic-tissue interactions in two benthic freshwater organisms
Researchers developed a histological toolbox to analyze microplastic-tissue interactions in two benthic freshwater invertebrates, addressing the methodological gap in available protocols for detecting whether ingested microplastics simply pass through the gut or accumulate at specific tissue zones and translocate into organism tissues.