TY - JOUR A1 - Rühle, Bastian A1 - Krumrey, Julian Frederic A1 - Hodoroaba, Vasile-Dan T1 - Workflow towards automated segmentation of agglomerated, non‑spherical particles from electron microscopy images using artificial neural networks JF - Scientific reports N2 - 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 minutes of hands-on time by a user and can typically be finished within less than 12 hours 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. KW - Electron microscopy KW - Neural networks KW - Artificial intelligence KW - Image segmentation KW - Automated image analysis PY - 2021 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-522454 DO - https://doi.org/10.1038/s41598-021-84287-6 VL - 11 IS - 1 SP - 4942 PB - Springer Nature AN - OPUS4-52245 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Rühle, Bastian A1 - Hodoroaba, Vasile-Dan T1 - Towards Automated Electron Microscopy Image Segmentation for Nanoparticles of Complex Shape by Convolutional Neural Networks JF - Microscopy and Microanalysis N2 - In this contribution different ways are explored with the aim to generate suitable training data for ‘non-ideal’ samples using various approaches, e.g., computer-generated images or unsupervised learning algorithms such as generative adversarial networks (GANs). We used these data to train simple CNNs to produce segmentation masks of SEM images and tested the trained networks on real SEM images of complex nanoparticle samples. The novel use of CNN for the automated analysis of the size of nanoparticles of complex shape and with a high degree of agglomeration has proved to be a promising tool for the evaluation of particle size distribution on a large number of constituent particles. Further development and validation of the preliminary model, respectively larger training and validation data sets are necessary. KW - Nanoparticles KW - Convolutional neural networks KW - Image segmentation KW - Electron microscopy KW - Automatisation PY - 2020 DO - https://doi.org/10.1017/S1431927620017262 VL - 26 IS - S2 SP - 1188 EP - 1189 PB - Cambridge University Press CY - Cambridge, UK AN - OPUS4-51773 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Saleh, Maysoon I. A1 - Rühle, Bastian A1 - Wang, Shu A1 - Radnik, Jörg A1 - You, Yi A1 - Resch-Genger, Ute T1 - Assessing the protective effects of different surface coatings on NaYF4:YB3+, Er3+, upconverting nanoparticles in buffer and DMEM JF - Scientific reports N2 - We studied the dissolution behavior of β NaYF4:Yb(20%), Er(2%) UCNP of two different sizes in biologically relevant media i.e., water (neutral pH), phosphate buffered saline (PBS), and Dulbecco’s modified Eagle medium (DMEM) at different temperatures and particle concentrations. Special emphasis was dedicated to assess the influence of different surface functionalizations, particularly the potential of mesoporous and microporous silica shells of different thicknesses for UCNP stabilization and protection. Dissolution was quantified electrochemically using a fluoride ion selective electrode (ISE) and by inductively coupled plasma optical emission spectrometry (ICP OES). In addition, dissolution was monitored fluorometrically. These experiments revealed that a thick microporous silica shell drastically decreased dissolution. Our results also underline the critical influence of the chemical composition of the aqueous environment on UCNP dissolution. In DMEM, we observed the formation of a layer of adsorbed molecules on the UCNP surface that protected the UCNP from dissolution and enhanced their fluorescence. Examination of this layer by X ray photoelectron spectroscopy (XPS) and mass spectrometry (MS) suggested that mainly phenylalanine, lysine, and glucose are adsorbed from DMEM. These findings should be considered in the future for cellular toxicity studies with UCNP and other nanoparticles and the design of new biocompatible surface coatings. KW - Fluorescence KW - Lifetime KW - Method KW - Quantification KW - Stability KW - Coating KW - Surface chemistry KW - Lanthanide KW - Fluoride KW - Electrochemistry KW - ICP-OES KW - Upconversion KW - Nano KW - Particle KW - Aging KW - Quality assurance KW - Mass spectrometry KW - XPS PY - 2020 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-515984 DO - https://doi.org/10.1038/s41598-020-76116-z SN - 2045-2322 VL - 10 IS - 1 SP - 19318-1 EP - 19318-11 PB - Springer Nature CY - London AN - OPUS4-51598 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Mrkwitschka, Paul A1 - Abram, Sarah-Luise A1 - Thünemann, Andreas A1 - Rühle, Bastian A1 - Radnik, Jörg A1 - Bresch, Harald A1 - Resch-Genger, Ute A1 - Hodoroaba, Vasile-Dan T1 - The Role of Electron Microscopy in the Development of Monodisperse Cubic Iron Oxide Nanoparticles as Certified Reference Material for Size and Shape JF - Microscopy and Microanalysis N2 - BAM is currently building up a platform of novel nanoRMs relying on iron oxide nanoparticles of different shape, size and surface chemistry. Iron oxide was chosen as a core material because of its relevance to the material and life sciences. As a first candidate of this series, we present cubic iron oxide nanoparticles with a nominal edge length of 8 nm. These particles were synthesized by thermal decomposition of iron oleate in high boiling organic solvents adapting well-known literature procedures. After dilution to a concentration suitable for electron microscopy (TEM and SEM) as well as for small-angle X-ray scattering (SAXS) measurements, the candidate nanoRM was bottled and assessed for homogeneity and stability by both methods following the guidelines of ISO 17034 and ISO Guide 35. The particle sizes obtained by both STEM-in-SEM and TEM are in excellent agreement with a minimum Feret of 8.3 nm ± 0.7 nm. The aspect ratio (AR) of the iron oxide cubes were extracted from the images as the ratio of minimum Feret to Feret resulting in an AR of 1.18 for TEM to 1.25 for SEM. Alternatively, a rectangular bounding box was fitted originating from the minimum Feret and the longest distance through the particle in perpendicular direction. This led to AR values of 1.05 for TEM and 1.12 for SEM, respectively. The results confirm the almost ideal cubic shape. KW - Reference nanoparticles KW - Iron oxide KW - Cubical shape KW - Electron microscopy KW - SAXS KW - Nano CRM KW - Size PY - 2022 DO - https://doi.org/10.1017/S1431927622003610 SN - 1435-8115 VL - 28 IS - Suppl. 1 SP - 802 EP - 805 PB - Cambridge University Press AN - OPUS4-55599 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -