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 segmentation of anatomical structures in fetal ultrasound video is challenging due to the highly diverse appearance of anatomies and image quality. In this paper, we propose an ultrasound video anatomy segmentation approach to iteratively memorise and segment incoming video frames, which is suitable for online segmentation. This is achieved by a spatio-temporal model that utilizes an adaptive memory bank to store the segmentation history of preceding frames to assist the current frame segmentation. The memory is updated adaptively using a skip gate mechanism based on segmentation confidence, preserving only high-confidence predictions for future use. We evaluate our approach and related state-of-the-art methods on a clinical dataset. The experimental results demonstrate that our method achieves superior performance with an F1 score of 84.83%. Visually, the use of adaptive temporal memory also aids in reducing error accumulation during video segmentation.

Original publication




Conference paper

Publication Date



14337 LNCS


3 - 12