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

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 optimise therapeutic approaches for pathological pregnancies. In addition, the methodology presented is applicable in characterisation 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.SummaryUsing the human placenta as an example, this study brings together sophisticated sample preparation protocols, advanced 3D X-ray imaging, and robust, validated machine-learning segmentation techniques to provide massively multi-scale and multi-domain insights on vascular-rich organ morphology and function.

Comparison of Methods to Segment Variable-Contrast XCT Images of Methane-Bearing Sand Using U-Nets Trained on Single Dataset Sub-Volumes

“…The input image size for the model was again set to 572 × 572 pixels. When predicting the segmentations for the analysis region, an averaging approach for data produced from each plane was used as described by Tun et al [59], but with a modification to take the multiple labels into account. In short, this averaging approach consisted in slicing, segmenting, and rotating the volume across the XY 4-fold symmetry plane and then splitting and hierarchically recombining the 12 resulting segmentation volumes so that two label volumes were obtained, one containing labels for sand vs. background and the other for CH 4 vs. background.…”