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 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.
Digital Images with 3D Geometry from Data Compression by Multi-scale Representations of B-Spline Surfaces