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.

We propose an automated framework for predicting age and neurodevelopmental maturation of a fetus based on 3D ultrasound (US) brain image appearance. A topology-preserving manifold representation of the fetal skull enabled design of bespoke scale-invariant image features. Our regression forest model used these features to learn a mapping from age-related sonographic image patterns to fetal age and development. The Sylvian Fissure was identified as a critical region for accurate age estimation, and restricting the search space to this anatomy improved prediction accuracy on a set of 130 healthy fetuses (error ± 3.8 days; r = 0.98 performing the best current clinical method. Our framework remained robust when applied to a routine clinical population.

Type

Journal article

Journal

Med Image Comput Comput Assist Interv

Publication Date

2014

Volume

17

Pages

260 - 267