  Automatic segmentation of the placenta in fetal ultrasound (US) is
challenging due to the (i) high diversity of placenta appearance, (ii) the
restricted quality in US resulting in highly variable reference annotations,
and (iii) the limited field-of-view of US prohibiting whole placenta assessment
at late gestation. In this work, we address these three challenges with a
multi-task learning approach that combines the classification of placental
location (e.g., anterior, posterior) and semantic placenta segmentation in a
single convolutional neural network. Through the classification task the model
can learn from larger and more diverse datasets while improving the accuracy of
the segmentation task in particular in limited training set conditions. With
this approach we investigate the variability in annotations from multiple
raters and show that our automatic segmentations (Dice of 0.86 for anterior and
0.83 for posterior placentas) achieve human-level performance as compared to
intra- and inter-observer variability. Lastly, our approach can deliver whole
placenta segmentation using a multi-view US acquisition pipeline consisting of
three stages: multi-probe image acquisition, image fusion and image
segmentation. This results in high quality segmentation of larger structures
such as the placenta in US with reduced image artifacts which are beyond the
field-of-view of single probes.
