Automatic 3D Multi-modal Ultrasound Segmentation of Human Placenta using
  Fusion Strategies and Deep Learning
Sonit Singh Gordon Stevenson Brendan Mein Alec Welsh Arcot Sowmya
Purpose: Ultrasound is the most commonly used medical imaging modality for
diagnosis and screening in clinical practice. Due to its safety profile,
noninvasive nature and portability, ultrasound is the primary imaging modality
for fetal assessment in pregnancy. Current ultrasound processing methods are
either manual or semi-automatic and are therefore laborious, time-consuming and
prone to errors, and automation would go a long way in addressing these
challenges. Automated identification of placental changes at earlier gestation
could facilitate potential therapies for conditions such as fetal growth
restriction and pre-eclampsia that are currently detected only at late
gestational age, potentially preventing perinatal morbidity and mortality.
  Methods: We propose an automatic three-dimensional multi-modal (B-mode and
power Doppler) ultrasound segmentation of the human placenta using deep
learning combined with different fusion strategies.We collected data containing
Bmode and power Doppler ultrasound scans for 400 studies.
  Results: We evaluated different fusion strategies and state-of-the-art image
segmentation networks for placenta segmentation based on standard overlap- and
boundary-based metrics. We found that multimodal information in the form of
B-mode and power Doppler scans outperform any single modality. Furthermore, we
found that B-mode and power Doppler input scans fused at the data level provide
the best results with a mean Dice Similarity Coefficient (DSC) of 0.849.
  Conclusion: We conclude that the multi-modal approach of combining B-mode and
power Doppler scans is effective in segmenting the placenta from 3D ultrasound
scans in a fully automated manner and is robust to quality variation of the
datasets.