Assessing Automated Vessel Segmentation Techniques of Feto-Placental Vasculature from MRI

Motivation: Placental insufficiency is a factor that contributes to multiple pregnancy complications such as Fetal Growth Restriction (FGR). Goal(s): Providing better understanding to clinicians is important for future treatment planning and automatic detection of the feto-placental vasculature from imaging may provide a tool to guide clinical assessment. Approach: This work compared the Frangi filter and an edge-based detection algorithm abilities to automatically identify feto-placental vasculature from MRI. Results: The study found that both methods identified likely vascular structures, and both showed spatial trend similarities when compared with gold-standard/high resolution Micro-CT, as well as a showing differences between FGR and Control vessel segmentations. Impact: Evaluating the most accurate method for automatically identifying feto-placental vasculature will go on to further aid quantifying placental insufficiency and improving understanding for predicting and clinically treating conditions such as fetal growth restriction.