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Three-dimensional reconstruction of porous polymer films from FIB-SEM nanotomography data using random forests
RISE Research Institutes of Sweden, Bioekonomi och hälsa, Jordbruk och livsmedel. Chalmers University of Technology, Sweden; University of Gothenburg, Sweden.ORCID-id: 0000-0002-5956-9934
Chalmers University of Technology, Sweden.
AstraZeneca, Sweden.
AstraZeneca, Sweden.
Vise andre og tillknytning
2021 (engelsk)Inngår i: Journal of Microscopy, ISSN 0022-2720, E-ISSN 1365-2818, Vol. 281, nr 1, s. 76-86Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Combined focused ion beam and scanning electron microscope (FIB-SEM) tomography is a well-established technique for high resolution imaging and reconstruction of the microstructure of a wide range of materials. Segmentation of FIB-SEM data is complicated due to a number of factors; the most prominent is that for porous materials, the scanning electron microscope image slices contain information not only from the planar cross-section of the material but also from underlying, exposed subsurface pores. In this work, we develop a segmentation method for FIB-SEM data from ethyl cellulose porous films made from ethyl cellulose and hydroxypropyl cellulose (EC/HPC) polymer blends. These materials are used for coating pharmaceutical oral dosage forms (tablets or pellets) to control drug release. We study three samples of ethyl cellulose and hydroxypropyl cellulose with different volume fractions where the hydroxypropyl cellulose phase has been leached out, resulting in a porous material. The data are segmented using scale-space features and a random forest classifier. We demonstrate good agreement with manual segmentations. The method enables quantitative characterization and subsequent optimization of material structure for controlled release applications. Although the methodology is demonstrated on porous polymer films, it is applicable to other soft porous materials imaged by FIB-SEM. We make the data and software used publicly available to facilitate further development of FIB-SEM segmentation methods. Lay Description: For imaging of very fine structures in materials, the resolution limits of, e.g. X-ray computed tomography quickly become a bottleneck. Scanning electron microscopy (SEM) provides a way out, but it is essentially a two-dimensional imaging technique. One manner in which to extend it to three dimensions is to use a focused ion beam (FIB) combined with a scanning electron microscopy and acquire tomography data. In FIB-SEM tomography, ions are used to perform serial sectioning and the electron beam is used to image the cross section surface. This is a well-established method for a wide range of materials. However, image analysis of FIB-SEM data is complicated for a variety of reasons, in particular for porous media. In this work, we analyse FIB-SEM data from ethyl cellulose porous films made from ethyl cellulose and hydroxypropyl cellulose (EC/HPC) polymer blends. These films are used as coatings for controlled drug release. The aim is to perform image segmentation, i.e. to identify which parts of the image data constitute the pores and the solid, respectively. Manual segmentation, i.e. when a trained operator manually identifies areas constituting pores and solid, is too time-consuming to do in full for our very large data sets. However, by performing manual segmentation on a set of small, random regions of the data, we can train a machine learning algorithm to perform automatic segmentation on the entire data sets. The method yields good agreement with the manual segmentations and yields porosities of the entire data sets in very good agreement with expected values. The method facilitates understanding and quantitative characterization of the geometrical structure of the materials, and ultimately understanding of how to tailor the drug release. © 2020 The Authors.

sted, utgiver, år, opplag, sider
Blackwell Publishing Ltd , 2021. Vol. 281, nr 1, s. 76-86
Emneord [en]
Controlled drug release, FIB-SEM, Image analysis, Machine learning, Microstructure, Polymer films, Porous materials, Random forest, Segmentation
HSV kategori
Identifikatorer
URN: urn:nbn:se:ri:diva-48544DOI: 10.1111/jmi.12950Scopus ID: 2-s2.0-85088014477OAI: oai:DiVA.org:ri-48544DiVA, id: diva2:1466020
Merknad

Funding details: 2019‐01295; Funding details: VetenskapsrÃ¥det, VR, 2016‐03809; Funding details: Stiftelsen för Strategisk Forskning, SSF; Funding text 1: The financial support of the Swedish Research Council (grant number 2016‐03809), the Swedish Research Council for Sustainable Development (grant number 2019‐01295), the Swedish Foundation for Strategic Research (the project ‘Material structures seen through microscopes and statistics'), the VINN Excellence Centre SuMo Biomaterials and Chalmers Area of Advance Materials Science is acknowledged. Further, we acknowledge AstraZeneca for providing the samples and Chalmers Material Analysis Laboratory for their support of microscopes. The computations were in part performed on resources at Chalmers Centre for Computational Science and Engineering (C3SE) provided by the Swedish National Infrastructure for Computing (SNIC). A GPU used for part of this research was donated by the NVIDIA Corporation.

Tilgjengelig fra: 2020-09-10 Laget: 2020-09-10 Sist oppdatert: 2023-05-26bibliografisk kontrollert

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