AutoEM: a software for automated acquisition and analysis of nanoparticles

Uusimaeki, Toni; Wagner, Tobias; Lipinski, Hans‐Gerd; Kaegi, Rälf

doi:10.1007/s11051-019-4555-9

Cited by 22 publications

(20 citation statements)

References 45 publications

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“…The first column consists of the upper limit of the particle size classes, the second contains the cumulative distribution function value. This format is featured by the ParticleSizer ( (access 2019-08-16)) [50] (an ImageJ ( (access 2019-08-16)) [51] plug-in developed within NanoDefine) for TEM image analysis that can be applied for TEM-based particulate material size measurement approaches [52] and is part of AutoEM [53]. Data generated by the Single Particle Calculation tool ( (access 2019-08-16)) for calculation and evaluation of spICP-MS data [54] is accepted as well.…”

Section: Resultsmentioning

confidence: 99%

NanoDefiner e-Tool: An Implemented Decision Support Framework for Nanomaterial Identification

et al. 2019

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The European Commission’s recommendation on the definition of nanomaterial (2011/696/EU) established an applicable standard for material categorization. However, manufacturers face regulatory challenges during registration of their products. Reliable categorization is difficult and requires considerable expertise in existing measurement techniques (MTs). Additionally, organizational complexity is increased as different authorities’ registration processes require distinct reporting. The NanoDefine project tackled these obstacles by providing the NanoDefiner e-tool: A decision support expert system for nanomaterial identification in a regulatory context. It provides MT recommendations for categorization of specific materials using a tiered approach (screening/confirmatory), and was constructed with experts from academia and industry to be extensible, interoperable, and adaptable for forthcoming revisions of the nanomaterial definition. An implemented MT-driven material categorization scheme allows detailed description. Its guided workflow is suitable for a variety of user groups. Direct feedback and explanation enable transparent decisions. Expert knowledge is held in a knowledge base for representation of MT performance criteria and physicochemical particle type properties. Continuous revision ensured data quality and validity. Recommendations were validated by independent case studies on industry-relevant particulate materials. Besides supporting material identification and registration, the free and open-source e-tool may serve as template for other expert systems within the nanoscience domain.

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Section: Resultsmentioning

confidence: 99%

NanoDefiner e-Tool: An Implemented Decision Support Framework for Nanomaterial Identification

et al. 2019

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“…In recent years, artificial neural networks (ANNs) and especially convolutional neural networks (CNNs) have shown enormous potential in complex computer vision tasks such as image classification and segmentation, and their scope has been extended towards automated image analysis of biomedical and life science data including light microscopy images, cryo TEM, or CT/MRI tomography [7][8][9][10][11][12][13] . The drawback of such deep learning algorithms is the need for large training (and ideally also validation) datasets containing high quality annotated images to train the algorithms via backpropagation in supervised learning.…”

Section: Workflow Towards Automated Segmentation Of Agglomerated Nonmentioning

confidence: 99%

Workflow towards automated segmentation of agglomerated, non-spherical particles from electron microscopy images using artificial neural networks

Rühle

Krumrey

Hodoroabă

2021

Sci Rep

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We present a workflow for obtaining fully trained artificial neural networks that can perform automatic particle segmentations of agglomerated, non-spherical nanoparticles from scanning electron microscopy images “from scratch”, without the need for large training data sets of manually annotated images. The whole process only requires about 15 min of hands-on time by a user and can typically be finished within less than 12 h when training on a single graphics card (GPU). After training, SEM image analysis can be carried out by the artificial neural network within seconds. This is achieved by using unsupervised learning for most of the training dataset generation, making heavy use of generative adversarial networks and especially unpaired image-to-image translation via cycle-consistent adversarial networks. We compare the segmentation masks obtained with our suggested workflow qualitatively and quantitatively to state-of-the-art methods using various metrics. Finally, we used the segmentation masks for automatically extracting particle size distributions from the SEM images of TiO2 particles, which were in excellent agreement with particle size distributions obtained manually but could be obtained in a fraction of the time.

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“…However, these approaches are low throughput, which limits-even the most sophisticated automated setups-to PNC detection limits of 10 6 particles mL -1 , and the detection of target particle types/compositions P against an overwhelming number of 'background' NPs, as is observed in natural samples, is especially challenging. 19 Single-particle-ICP-MS enables direct measurement of analyte element mass in individual NPs and offers high throughput detection, with PNC detection limits down to 10 2 particles mL -1 . However, sp-ICP-MS alone provides no information on particle size or morphology.…”

Section: Introductionmentioning

confidence: 99%

“…However, these approaches are low throughput, which limits—even the most sophisticated automated setups—to PNC detection limits of ∼10 6 particles mL −1 , and the detection of target particle types/compositions against an overwhelming number of ‘background’ NPs, as is observed in natural samples, is especially challenging. 19 …”

Section: Introductionmentioning

confidence: 99%

Emerging investigator series: automated single-nanoparticle quantification and classification: a holistic study of particles into and out of wastewater treatment plants in Switzerland

Mehrabi

Kaegi

Günther

et al. 2021

Environ. Sci.: Nano

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AutoEM: a software for automated acquisition and analysis of nanoparticles

Cited by 22 publications

References 45 publications

NanoDefiner e-Tool: An Implemented Decision Support Framework for Nanomaterial Identification

NanoDefiner e-Tool: An Implemented Decision Support Framework for Nanomaterial Identification

Workflow towards automated segmentation of agglomerated, non-spherical particles from electron microscopy images using artificial neural networks

Emerging investigator series: automated single-nanoparticle quantification and classification: a holistic study of particles into and out of wastewater treatment plants in Switzerland

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