Deep-learning (DL) algorithms are becoming the standard for processing ultrasound (US)
fetal images. A number of survey papers in the field is today available, but most of them are …

Artificial intelligence (AI) has gained prominence in medical imaging, particularly in
obstetrics and gynecology (OB/GYN), where ultrasound (US) is the preferred method. It is …

We present the first system that provides real-time probe movement guidance for acquiring
standard planes in routine freehand obstetric ultrasound scanning. Such a system can …

Ultrasound is the primary modality for obstetric imaging and is highly sonographer
dependent. Long training period, insufficient recruitment and poor retention of sonographers …

The segmentation of the fetal head (FH) and pubic symphysis (PS) from intrapartum
ultrasound images plays a pivotal role in monitoring labor progression and informing crucial …

In this work, we present a novel gaze-assisted natural language processing (NLP)-based
video captioning model to describe routine second-trimester fetal ultrasound scan videos in …

Objective. This work investigates the use of deep convolutional neural networks (CNN) to
automatically perform measurements of fetal body parts, including head circumference …

Segmentation of the fetal and maternal structures, particularly intrapartum ultrasound
imaging as advocated by the International Society of Ultrasound in Obstetrics and …

Obstetric ultrasound assessment of fetal anatomy in the first trimester of pregnancy is one of
the less explored fields in obstetric sonography because of the paucity of guidelines on …

Self-supervised contrastive representation learning offers the advantage of learning
meaningful visual representations from unlabeled medical datasets for transfer learning …