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Convolutional neura...
Convolutional neural networks for segmentation of FIB-SEM nanotomography data from porous polymer films for controlled drug release
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- Skärberg, F. (författare)
- RISE,Jordbruk och livsmedel
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- Fager, Cecilia (författare)
- KTH,Fiber- och polymerteknologi,Department of Physics, Chalmers University of Technology, Göteborg, Sweden,Chalmers tekniska högskola,Chalmers University of Technology,Kungliga Tekniska Högskolan (KTH),Royal Institute of Technology (KTH),Chalmers University of Technology, Sweden; KTH Royal Institute of Technology, Sweden
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- Mendoza-Lara, F. (författare)
- AstraZeneca AB
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- Josefson, M. (författare)
- AstraZeneca AB,AstraZeneca, Sweden
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- Olsson, Eva, 1960 (författare)
- Chalmers tekniska högskola,Chalmers University of Technology,Chalmers University of Technology, Sweden
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- Lorén, Niklas, 1970 (författare)
- RISE,Jordbruk och livsmedel,Chalmers University of Technology, Sweden
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- Röding, Magnus, 1984 (författare)
- RISE,Gothenburg University,Göteborgs universitet,Institutionen för matematiska vetenskaper,Department of Mathematical Sciences,Jordbruk och livsmedel,Chalmers University of Technology, Sweden; University of Gothenburg, Sweden
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(creator_code:org_t)
- 2021-05-04
- 2021
- Engelska.
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Ingår i: Journal of Microscopy. - : John Wiley and Sons Inc. - 0022-2720 .- 1365-2818. ; 283:1, s. 51-63
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Abstract
Ämnesord
Stäng
- Phase-separated polymer films are commonly used as coatings around pharmaceutical oral dosage forms (tablets or pellets) to facilitate controlled drug release. A typical choice is to use ethyl cellulose and hydroxypropyl cellulose (EC/HPC) polymer blends. When an EC/HPC film is in contact with water, the leaching out of the water-soluble HPC phase produces an EC film with a porous network through which the drug is transported. The drug release can be tailored by controlling the structure of this porous network. Imaging and characterization of such EC porous films facilitates understanding of how to control and tailor film formation and ultimately drug release. Combined focused ion beam and scanning electron microscope (FIB-SEM) tomography is a well-established technique for high-resolution imaging, and suitable for this application. However, for segmenting image data, in this case to correctly identify the porous network, FIB-SEM is a challenging technique to work with. In this work, we implement convolutional neural networks for segmentation of FIB-SEM image data. The data are acquired from three EC porous films where the HPC phases have been leached out. The three data sets have varying porosities in a range of interest for controlled drug release applications. We demonstrate very good agreement with manual segmentations. In particular, we demonstrate an improvement in comparison to previous work on the same data sets that utilized a random forest classifier trained on Gaussian scale-space features. Finally, we facilitate further development of FIB-SEM segmentation methods by making the data and software used open access.
Ämnesord
- NATURVETENSKAP -- Kemi -- Polymerkemi (hsv//swe)
- NATURAL SCIENCES -- Chemical Sciences -- Polymer Chemistry (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Kemiteknik -- Polymerteknologi (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Chemical Engineering -- Polymer Technologies (hsv//eng)
- NATURVETENSKAP -- Kemi -- Annan kemi (hsv//swe)
- NATURAL SCIENCES -- Chemical Sciences -- Other Chemistry Topics (hsv//eng)
- NATURVETENSKAP -- Matematik (hsv//swe)
- NATURAL SCIENCES -- Mathematics (hsv//eng)
Nyckelord
- controlled drug release
- convolutional neural networks
- deep learning
- focused ion beam scanning electron microscopy
- image analysis
- machine learning
- microstructure
- polymer films
- porous materials
- semantic segmentation
- alcohol
- ethyl cellulose
- hydroxypropylcellulose
- porous polymer
- polymer
- water
- Article
- artificial neural network
- comparative study
- controlled study
- convolutional neural network
- data accuracy
- electron beam
- feature extraction
- focused ion beam
- image segmentation
- kernel method
- molecular weight
- prediction
- scanning electron microscopy
- drug release
- porosity
- Drug Liberation
- Neural Networks
- Computer
- Polymers
- controlled drug release
- convolutional neural networks
- deep learning
- focused ion beam scanning electron microscopy
- image analysis
- machine learning
- microstructure
- polymer films
- porous materials
- semantic segmentation
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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Till lärosätets databas
- Av författaren/redakt...
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Skärberg, F.
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Fager, Cecilia
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Mendoza-Lara, F.
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Josefson, M.
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Olsson, Eva, 196 ...
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Lorén, Niklas, 1 ...
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visa fler...
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Röding, Magnus, ...
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visa färre...
- Om ämnet
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- NATURVETENSKAP
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NATURVETENSKAP
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och Kemi
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och Polymerkemi
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- TEKNIK OCH TEKNOLOGIER
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TEKNIK OCH TEKNO ...
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och Kemiteknik
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och Polymerteknologi
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- NATURVETENSKAP
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NATURVETENSKAP
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och Kemi
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och Annan kemi
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- NATURVETENSKAP
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NATURVETENSKAP
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och Matematik
- Artiklar i publikationen
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Journal of Micro ...
- Av lärosätet
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Kungliga Tekniska Högskolan
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Chalmers tekniska högskola
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Göteborgs universitet
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RISE