TY - CONF A1 - Rühle, Bastian A1 - Hodoroaba, Vasile-Dan T1 - Automatic Image Segmentation and Analysis using Neural Networks N2 - We present a workflow for obtaining fully trained artificial neural networks that can perform automatic particle segmentations of agglomerated, non-spherical nanoparticles from electron microscopy images “from scratch”, without the need for large training data sets of manually annotated images. 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. 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, and the segmented images can be used for automatically extracting and calculating various other particle size and shape descriptors. T2 - Machine Learning Workshop CY - Online Meeting DA - 18.03.2021 KW - Electron Microscopy KW - Neural Networks KW - Artificial Intelligence KW - Image Segmentation KW - Automated Image Analysis PY - 2021 AN - OPUS4-52304 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -