  Semantic contour detection is a challenging problem that is often met in
medical imaging, of which placental image analysis is a particular example. In
this paper, we investigate utero-placental interface (UPI) detection in 2D
placental ultrasound images by formulating it as a semantic contour detection
problem. As opposed to natural images, placental ultrasound images contain
specific anatomical structures thus have unique geometry. We argue it would be
beneficial for UPI detectors to incorporate global context modelling in order
to reduce unwanted false positive UPI predictions. Our approach, namely
UPI-Net, aims to capture long-range dependencies in placenta geometry through
lightweight global context modelling and effective multi-scale feature
aggregation. We perform a subject-level 10-fold nested cross-validation on a
placental ultrasound database (4,871 images with labelled UPI from 49 scans).
Experimental results demonstrate that, without introducing considerable
computational overhead, UPI-Net yields the highest performance in terms of
standard contour detection metrics, compared to other competitive benchmarks.
