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 -