A massively multi-scale approach to characterizing tissue architecture by synchrotron micro-CT applied to the human placenta

WM Tun, Gowsihan Poologasundarampillai, H Bischof, G Nye, ONF King, M Basham, Yasuaki Tokudome, RM Lewis, ED Johnstone, P Brownbill, M Darrow, IL Chernyavsky

Research output: Contribution to journalArticlepeer-review

Abstract

Multi-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.
Original languageEnglish
Article number20210140
Number of pages12
JournalJournal of The Royal Society Interface
Volume18
Issue number179
DOIs
Publication statusPublished - 2 Jun 2021

Keywords

  • human placenta
  • computed tomography
  • contrast agent
  • machine-learning
  • segmentation
  • flow network
  • spatial statistics

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