  Blood oxygen level dependent (BOLD) MRI time series with maternal hyperoxia
can assess placental oxygenation and function. Measuring precise BOLD changes
in the placenta requires accurate temporal placental segmentation and is
confounded by fetal and maternal motion, contractions, and hyperoxia-induced
intensity changes. Current BOLD placenta segmentation methods warp a manually
annotated subject-specific template to the entire time series. However, as the
placenta is a thin, elongated, and highly non-rigid organ subject to large
deformations and obfuscated edges, existing work cannot accurately segment the
placental shape, especially near boundaries. In this work, we propose a machine
learning segmentation framework for placental BOLD MRI and apply it to
segmenting each volume in a time series. We use a placental-boundary weighted
loss formulation and perform a comprehensive evaluation across several popular
segmentation objectives. 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. Biomedically, our model performs reliably in segmenting
volumes in both normoxic and hyperoxic points in the BOLD time series. We
further find that boundary-weighting increases placental segmentation
performance by 8.3% and 6.0% Dice coefficient for the cross-entropy and signed
distance transform objectives, respectively. Our code and trained model is
available at https://github.com/mabulnaga/automatic-placenta-segmentation.
