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), outperforming the best current clinical method. Our framework remained robust when applied to a routine clinical population. © 2014 Springer International Publishing.
CITATION STYLE
Namburete, A. I. L., Yaqub, M., Kemp, B., Papageorghiou, A. T., & Noble, J. A. (2014). Predicting fetal neurodevelopmental age from ultrasound images. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8674 LNCS, pp. 260–267). Springer Verlag. https://doi.org/10.1007/978-3-319-10470-6_33
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