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Dataset and code used in F Skärberg, et al, "Convolutional neural networks for segmentation of FIB-SEM nanotomography data from porous polymer films for controlled drug release", published in Journal of Microscopy. In this work, we develop a segmentation method based on convolutional neural networks (CNNs) for focused ion beam scanning electron microscopy (FIB-SEM) data, acquired from porous polymer films made from ethyl cellulose and hydroxypropyl cellulose (EC/HPC) polymer blends. Herein, all codes in Python/Tensorflow and Matlab necessary to reproduce the results of the paper are supplied, together with the raw data, manual segmentations, trained models, and final segmentation results.
polymer films, hydroxypropyl cellulose, focused ion beam scanning electron microscopy, deep learning, convolutional neural network, semantic segmentation, ethyl cellulose, machine learning, image analysis, controlled release, porous materials, artificial neural network
polymer films, hydroxypropyl cellulose, focused ion beam scanning electron microscopy, deep learning, convolutional neural network, semantic segmentation, ethyl cellulose, machine learning, image analysis, controlled release, porous materials, artificial neural network
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