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

Abstract 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 anno...

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Autores principales:	Bastian Rühle, Julian Frederic Krumrey, Vasile-Dan Hodoroaba
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/5f1227f7c1114b9b8aea7aff6a12191a
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spelling	oai:doaj.org-article:5f1227f7c1114b9b8aea7aff6a12191a2021-12-02T13:34:57ZWorkflow towards automated segmentation of agglomerated, non-spherical particles from electron microscopy images using artificial neural networks10.1038/s41598-021-84287-62045-2322https://doaj.org/article/5f1227f7c1114b9b8aea7aff6a12191a2021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-84287-6https://doaj.org/toc/2045-2322Abstract 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.Bastian RühleJulian Frederic KrumreyVasile-Dan HodoroabaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Bastian Rühle Julian Frederic Krumrey Vasile-Dan Hodoroaba Workflow towards automated segmentation of agglomerated, non-spherical particles from electron microscopy images using artificial neural networks
description	Abstract 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.
format	article
author	Bastian Rühle Julian Frederic Krumrey Vasile-Dan Hodoroaba
author_facet	Bastian Rühle Julian Frederic Krumrey Vasile-Dan Hodoroaba
author_sort	Bastian Rühle
title	Workflow towards automated segmentation of agglomerated, non-spherical particles from electron microscopy images using artificial neural networks
title_short	Workflow towards automated segmentation of agglomerated, non-spherical particles from electron microscopy images using artificial neural networks
title_full	Workflow towards automated segmentation of agglomerated, non-spherical particles from electron microscopy images using artificial neural networks
title_fullStr	Workflow towards automated segmentation of agglomerated, non-spherical particles from electron microscopy images using artificial neural networks
title_full_unstemmed	Workflow towards automated segmentation of agglomerated, non-spherical particles from electron microscopy images using artificial neural networks
title_sort	workflow towards automated segmentation of agglomerated, non-spherical particles from electron microscopy images using artificial neural networks
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/5f1227f7c1114b9b8aea7aff6a12191a
work_keys_str_mv	AT bastianruhle workflowtowardsautomatedsegmentationofagglomeratednonsphericalparticlesfromelectronmicroscopyimagesusingartificialneuralnetworks AT julianfrederickrumrey workflowtowardsautomatedsegmentationofagglomeratednonsphericalparticlesfromelectronmicroscopyimagesusingartificialneuralnetworks AT vasiledanhodoroaba workflowtowardsautomatedsegmentationofagglomeratednonsphericalparticlesfromelectronmicroscopyimagesusingartificialneuralnetworks
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Workflow towards automated segmentation of agglomerated, non-spherical particles from electron microscopy images using artificial neural networks

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