Workflow towards automated segmentation of agglomerated, non spherical particles from electron microscopy images using artificial neural networks.
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 min of hands on time by a user and can typically be finished within less than 12 h 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.
Scientific reports