Show simple item record

dc.contributor.authorTun, W. M
dc.contributor.authorPoologasundarampillai, G.
dc.contributor.authorBischof, H.
dc.contributor.authorNye, Gareth
dc.contributor.authorKing, O. N. F.
dc.contributor.authorBasham, M.
dc.contributor.authorTokudome, Y.
dc.contributor.authorLewis, R. M.
dc.contributor.authorJohnstone, E. D.
dc.contributor.authorBrownbill, Paul
dc.contributor.authorDarrow, M.
dc.contributor.authorChernyavsky, Igor
dc.date.accessioned2021-06-02T00:14:41Z
dc.date.available2021-06-02T00:14:41Z
dc.date.issued2021-06-02
dc.date.submitted2021-02-16
dc.identifierhttps://chesterrep.openrepository.com/bitstream/handle/10034/624809/rsif.2021.0140.xml?sequence=2
dc.identifierhttps://chesterrep.openrepository.com/bitstream/handle/10034/624809/rsif.2021.0140.pdf?sequence=3
dc.identifier.citationTun W. M., Poologasundarampillai G., Bischof H., Nye G., King O. N. F., Basham M., Tokudome Y., Lewis R. M., Johnstone E. D., Brownbill P., Darrow M. and Chernyavsky I. L. (2021). A massively multi-scale approach to characterizing tissue architecture by synchrotron micro-CT applied to the human placentaJ. Journal of the Royal Society Interface, 18(179), 20210140 http://doi.org/10.1098/rsif.2021.0140
dc.identifier.doi10.1098/rsif.2021.0140
dc.identifier.urihttp://hdl.handle.net/10034/624809
dc.descriptionFrom The Royal Society via Jisc Publications Router
dc.descriptionHistory: received 2021-02-16, accepted 2021-05-06, collection 2021-06, pub-electronic 2021-06-02
dc.descriptionArticle version: VoR
dc.descriptionPublication status: Published
dc.descriptionFunder: Engineering and Physical Sciences Research Council; Id: http://dx.doi.org/10.13039/501100000266; Grant(s): EP/M023877/1, EP/T008725/1
dc.descriptionFunder: Medical Research Council; Id: http://dx.doi.org/10.13039/501100000265; Grant(s): MR/N011538/1
dc.descriptionFunder: Wellcome Trust; Id: http://dx.doi.org/10.13039/100004440; Grant(s): 212980/Z/18/Z
dc.descriptionFunder: Great Britain Sasakawa Foundation; Id: http://dx.doi.org/10.13039/501100000625
dc.description.abstractMulti-scale structural assessment of biological soft tissue is challenging but essential to gain insight into structure–function relationships of tissue/organ. Using the human placenta as an example, this study brings together sophisticated sample preparation protocols, advanced imaging and robust, validated machine-learning segmentation techniques to provide the first massively multi-scale and multi-domain information that enables detailed morphological and functional analyses of both maternal and fetal placental domains. Finally, we quantify the scale-dependent error in morphological metrics of heterogeneous placental tissue, estimating the minimal tissue scale needed in extracting meaningful biological data. The developed protocol is beneficial for high-throughput investigation of structure–function relationships in both normal and diseased placentas, allowing us to optimize therapeutic approaches for pathological pregnancies. In addition, the methodology presented is applicable in the characterization of tissue architecture and physiological behaviours of other complex organs with similarity to the placenta, where an exchange barrier possesses circulating vascular and avascular fluid spaces.
dc.languageen
dc.publisherThe Royal Society
dc.rightsLicence for VoR version of this article: http://creativecommons.org/licenses/by/4.0/
dc.sourceeissn: 1742-5662
dc.subjectLife Sciences–Engineering interface
dc.subjectResearch articles
dc.subjecthuman placenta
dc.subjectcomputed tomography
dc.subjectcontrast agent
dc.subjectmachine-learning segmentation
dc.subjectflow network
dc.subjectspatial statistics
dc.titleA massively multi-scale approach to characterizing tissue architecture by synchrotron micro-CT applied to the human placenta
dc.typearticle
dc.date.updated2021-06-02T00:14:41Z
dc.identifier.urlhttps://royalsocietypublishing.org/doi/10.1098/rsif.2021.0140
dc.date.accepted2021-05-06


Files in this item

Thumbnail
Name:
rsif.2021.0140.xml
Size:
13.44Kb
Format:
XML
Thumbnail
Name:
rsif.2021.0140.pdf
Size:
1.430Mb
Format:
PDF

This item appears in the following Collection(s)

Show simple item record