Predicting Fetal Neurodevelopmental Age from Ultrasound Images

  • Ana I. L. Namburete
  • Mohammad Yaqub
  • Bryn Kemp
  • Aris T. Papageorghiou
  • J. Alison Noble
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8674)


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.


Head Circumference Fetal Brain Sylvian Fissure Tree Depth Midsagittal Plane 
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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Ana I. L. Namburete
    • 1
  • Mohammad Yaqub
    • 1
  • Bryn Kemp
    • 2
  • Aris T. Papageorghiou
    • 2
  • J. Alison Noble
    • 1
  1. 1.Institute of Biomedical Engineering, Dept of EngineeringUniversity of OxfordUK
  2. 2.Nuffield Department of Obstetrics and GynaecologyUniversity of OxfordUK

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