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 - 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 -