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Predicting Fetal Neurodevelopmental Age from Ultrasound Images

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Part of the Lecture Notes in Computer Science book series (LNIP,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

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© 2014 Springer International Publishing Switzerland

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Namburete, A.I.L., Yaqub, M., Kemp, B., Papageorghiou, A.T., Noble, J.A. (2014). Predicting Fetal Neurodevelopmental Age from Ultrasound Images. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014. MICCAI 2014. Lecture Notes in Computer Science, vol 8674. Springer, Cham.

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-10469-0

  • Online ISBN: 978-3-319-10470-6

  • eBook Packages: Computer ScienceComputer Science (R0)