Abstract
Reconstruction of highly porous structures from FIB-SEM image stacks is a difficult segmentation task. Supervised machine learning approaches demand large amounts of labeled data for training, that are hard to get in this case. A way to circumvent this problem is to train on simulated images. Here, we report on segmentation results derived by training a convolutional neural network solely on simulated FIB-SEM image stacks of realizations of a variety of stochastic geometry models.
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Fend, C., Moghiseh, A., Redenbach, C., Schladitz, K. (2021). Machine learning for reconstruction of highly porous structures from FIB-SEM nano-tomographic data. In: Beyerer, J., Maier, A., Niggemann, O. (eds) Machine Learning for Cyber Physical Systems. Technologien für die intelligente Automation, vol 13. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-62746-4_13
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