2019
DOI: 10.1111/jmi.12853
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Application of automated electron microscopy imaging and machine learning to characterise and quantify nanoparticle dispersion in aqueous media

Abstract: Summary For many nanoparticle applications it is important to understand dispersion in liquids. For nanomedicinal and nanotoxicological research this is complicated by the often complex nature of the biological dispersant and ultimately this leads to severe limitations in the analysis of the nanoparticle dispersion by light scattering techniques. Here we present an alternative analysis and associated workflow which utilises electron microscopy. The need to collect large, statistically relevant dat… Show more

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Cited by 25 publications
(19 citation statements)
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“…The current model, which excels at many NP detection tasks compared with traditional computer tools, is deployed on GitHub. Different from a shallow pixel classifier, 42 the performance of the deep neural network predictor can still be further improved by enriching the dataset to include more diverse cases, such as nonspherical particles, extremely large or small particles, and particles at image boundaries. Detection of particles of other shapes, such as nanotubes and nanolayers, can also be made possible, but the model has to be carefully retrained.…”
Section: Discussionmentioning
confidence: 99%
“…The current model, which excels at many NP detection tasks compared with traditional computer tools, is deployed on GitHub. Different from a shallow pixel classifier, 42 the performance of the deep neural network predictor can still be further improved by enriching the dataset to include more diverse cases, such as nonspherical particles, extremely large or small particles, and particles at image boundaries. Detection of particles of other shapes, such as nanotubes and nanolayers, can also be made possible, but the model has to be carefully retrained.…”
Section: Discussionmentioning
confidence: 99%
“…[62] Image profiling using open source CellProfiler and a CNN-based algorithm ilastik, [64] were employed by Ilet et al to study nanoparticle distributions. [65] A particularly impressive use of ML in nanosafety was published recently by Lazerovits et al [66] They conducted experiments aimed at understanding the adsorption of blood proteins on nanoparticles immediately after intravenous injection, how this interface changes during circulation, and how it alters to distribution of nanoparticles in vivo. They showed that the evolution of proteins on nanoparticle surfaces predicts the biological fate of nanoparticles in vivo.…”
Section: Examples Of the Application Of Ai And ML To Nanosafetymentioning
confidence: 99%
“…This can lead to particle size distributions after automated image analysis, and also to the quantification of agglomerates (number of primary particles, size, etc.) [13,17]. The classification of nanoparticles with respect to their shape is more difficult.…”
Section: Introductionmentioning
confidence: 99%