  Blood oxygen level dependent (BOLD) MRI with maternal hyperoxia can assess
oxygen transport within the placenta and has emerged as a promising tool to
study placental function. Measuring signal changes over time requires
segmenting the placenta in each volume of the time series. Due to the large
number of volumes in the BOLD time series, existing studies rely on
registration to map all volumes to a manually segmented template. As the
placenta can undergo large deformation due to fetal motion, maternal motion,
and contractions, this approach often results in a large number of discarded
volumes, where the registration approach fails. In this work, we propose a
machine learning model based on a U-Net neural network architecture to
automatically segment the placenta in BOLD MRI and apply it to segmenting each
volume in a time series. We use a boundary-weighted loss function to accurately
capture the placental shape. Our model is trained and tested on a cohort of 91
subjects containing healthy fetuses, fetuses with fetal growth restriction, and
mothers with high BMI. We achieve a Dice score of 0.83+/-0.04 when matching
with ground truth labels and our model performs reliably in segmenting volumes
in both normoxic and hyperoxic points in the BOLD time series. Our code and
trained model are available at
https://github.com/mabulnaga/automatic-placenta-segmentation.
