UPI-Net: Semantic Contour Detection in Placental Ultrasound
This addresses a specific challenge in medical imaging for placental analysis, but it is incremental as it builds on existing contour detection methods with domain-specific adaptations.
The paper tackles the problem of detecting the utero-placental interface (UPI) in placental ultrasound images by formulating it as semantic contour detection, and UPI-Net achieves the highest performance in standard metrics compared to benchmarks without significant computational overhead.
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.