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Dataset and code used in M. Röding, et al, "Three-dimensional reconstruction of porous polymer films from FIB-SEM nanotomography data using random forests", published in Journal of Microscopy, 2020. In this work, we develop a segmentation method for focused ion beam scanning electron microscopy (FIB-SEM) data acquired by volumetric imaging of porous polymer films made from ethyl cellulose and hydroxypropyl cellulose (EC/HPC) polymer blends. This type of polymer films are used for controlled release applications. Based on manual segmentation of a fraction of the data, a random forest classifier is trained and applied to the full data set. Here, raw data, manual segmentations, and the Matlab code used for all steps in the analysis are supplied.
polymer films, ethyl cellulose, machine learning, image analysis, hydroxypropyl cellulose, focused ion beam scanning electron microscopy, controlled release, porous materials, semantic segmentation, random forest
polymer films, ethyl cellulose, machine learning, image analysis, hydroxypropyl cellulose, focused ion beam scanning electron microscopy, controlled release, porous materials, semantic segmentation, random forest
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