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ZENODO
Dataset . 2021
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2021
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2021
License: CC BY
Data sources: ZENODO
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Convolutional neural networks for segmentation of FIB-SEM nanotomography data from porous polymer films for controlled drug release

Authors: Fredrik Skärberg; Cecilia Fager; Fransisco Mendoza-Lara; Mats Josefson; Eva Olsson; Niklas Lorén; Magnus Röding;

Convolutional neural networks for segmentation of FIB-SEM nanotomography data from porous polymer films for controlled drug release

Abstract

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.

Keywords

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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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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