Deep Learning Strategies for Ultrasound in Pregnancy

Abstract

Ultrasound is one of the most ubiquitous imaging modalities in clinical practice. It is cheap, does not require ionising radiation, and can be performed at the bedside, making it the most commonly used imaging technique in pregnancy. Despite these advantages, it does have some drawbacks such as relatively low imaging quality, low contrast, and high variability. With these constraints, automating the interpretation of ultrasound images is challenging. However, successfully automated identification of structures within three-dimensional ultrasound volumes has the potential to revolutionise clinical practice. For example, a small placental volume in the first trimester is correlated to adverse outcome later in pregnancy. If the placenta could be segmented reliably and automatically from a static three-dimensional ultrasound volume, it would facilitate the use of its estimated volume, and other morphological metrics, as part of a screening test for increased risk of pregnancy complications, potentially improving clinical outcomes. Recently, deep learning has emerged, achieving state-of-the-art performance in various research fields, notably medical image analysis involving classification, segmentation, object detection, and tracking tasks. Due to its increased performance with large datasets, deep learning has garnered great interest relating to medical imaging applications. In this review, the authors present an overview of deep learning methods applied to ultrasound in pregnancy, introducing their architectures and analysing strategies. Some common problems are presented and some perspectives into potential future research are provided.