TY - GEN A1 - Rühle, Bastian A1 - Krumrey, Julian Frederic A1 - Hodoroaba, Vasile-Dan T1 - Dataset accompanying the publication "Workflow towards automated segmentation of agglomerated, non-spherical particles from electron microscopy images using artificial neural networks" N2 - This dataset accompanies the following publication, first published in Scientific Reports (www.nature.com/articles/s41598-021-84287-6): B. Ruehle, J. Krumrey, V.-D. Hodoroaba, Scientific Reports, Workflow towards Automated Segmentation of Agglomerated, Non-Spherical Particles from Electron Microscopy Images using Artificial Neural Networks, DOI: 10.1038/s41598-021-84287-6 It contains electron microscopy micrographs of TiO2 particles, the corresponding segmentation masks, and their classifications into different categories depending on their visibility/occlusion. Please refer to the publication and its supporting information for more details on the acquisition and contents of the dataset, as well as the GitHub repository at https://github.com/BAMresearch/automatic-sem-image-segmentation KW - Electron microscopy KW - Neural networks KW - Image segmentation KW - Automated image analysis PY - 2021 DO - https://doi.org/10.5281/zenodo.4563942 PB - Zenodo CY - Geneva AN - OPUS4-52246 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 - TY - CONF A1 - Hodoroaba, Vasile-Dan A1 - Rühle, Bastian T1 - Towards automated electron microscopy image segmentation for nanoparticles of complex shape by convolutional neural networks 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. T2 - Microscopy and Microanalysis 2020 CY - Online meeting DA - 03.08.2020 KW - Nanoparticles KW - Automatisation KW - Image segmentation KW - Convolutional neural networks KW - Electron microscopy PY - 2020 AN - OPUS4-51114 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 - 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 -