Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

Automated ultrasound (US)-probe movement guidance is desirable to assist inexperienced human operators during obstetric US scanning. In this paper, we present a new visual-assisted probe movement technique using automated landmark retrieval for assistive obstetric US scanning. In a first step, a set of landmarks is constructed uniformly around a virtual 3D fetal model. Then, during obstetric scanning, a deep neural network (DNN) model locates the nearest landmark through descriptor search between the current observation and landmarks. The global position cues are visualised in real-time on a monitor to assist the human operator in probe movement. A Transformer-VLAD network is proposed to learn a global descriptor to represent each US image. This method abandons the need for deep parameter regression to enhance the generalization ability of the network. To avoid prohibitively expensive human annotation, anchor-positive-negative US image-pairs are automatically constructed through a KD-tree search of 3D probe positions. This leads to an end-to-end network trained in a self-supervised way through contrastive learning.

Original publication




Conference paper

Publication Date



12908 LNCS


670 - 679