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Abstract #4303

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

Joanna Chappell1, Magdalena Sokolska2, Rosalind Aughwane3, Alys R Clark4, Sebastien Ourselin 1, Anna L David3,5, and Andrew Melbourne1
1School of Biomedical Engineering and Imaging Sciences (BMEIS), Kings College London, London, United Kingdom, 2Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom, 3Elizabeth Garrett Anderson Institute for Women’s Health, University College London, London, United Kingdom, 4Auckland Bioengineering Institute, Auckland, New Zealand, 5University College London Hospital NHS Foundation Trust, London, United Kingdom

Synopsis

Keywords: Placenta, Placenta

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.

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Keywords

vesselsegmentationedgeplacentalvasculaturedetectionfilter