Quantitative characterization of rodent feto-placental vasculature morphology in micro-computed tomography images

Optimal development of placental vasculature is critical for fetal growth and health outcomes. Many studies characterizing the vascular structure of the fetal side of the placenta have utilized a range of two-dimensional and three-dimensional (3D) imaging techniques including X-ray micro-computed tomography (micro-CT) following perfusion of the vasculature with a radio-opaque compound. The CT approach has been used to study feto-placental vasculature in rodents and humans. Its inherent advantage is that it reveals the 3D structure in high resolution without destroying the sample. This permits both multiple scanning of the sample and follow-up histological investigations in the same sample. Nevertheless, the applicability of the approach is hampered both by the challenging segmentation of the vasculature and a lack of straightforward methodology to quantitate the feto-placental vascular network. This paper addresses these challenges.

An end-to-end methodology is presented for automatically segmenting the vasculature; obtaining a Strahler-ordered rooted-tree representation and extracting quantitative features from its nodes, segments and branches (including volume, length, tortuosity and branching angles). The methodology is demonstrated for rat and mouse placentas at the end of gestation (day 22 and day 18, respectively), perfused with Microfil® and imaged using two different micro-CT scanners.

The 3D visualizations of the resulting vascular trees clearly demonstrate differences between the branching complexity, tree span and tree depth of the mouse and rat placentas. The quantitative characterizations of these trees include not only the fundamental features that have been utilized in other studies of feto-placental vasculature but also several additional features. Boxplots of several of these-tortuosity, number of side branches, number of offspring per branch and branch volume-computed at each Strahler order are presented and interpreted. Differences and similarities between the mouse and rat casts are readily detected.

The proposed end-to-end methodology, and the implementation presented using a combination of Amira and Matlab, offers researchers in the field of placental vasculature characterization a straightforward and objective approach for quantifying micro-CT vascular datasets.