Advertisement

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)

Abstract

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.

Keywords

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.

References

  1. 1.
    ISUOG: Sonographic examination of the fetal central nervous system. Ultrasound Obstet. Gynecol. 29(1), 109–116 (2007)Google Scholar
  2. 2.
    Bottomley, C., Bourne, T.: Dating and growth in the first trimester. Best Pract. Res. Cl Ostet. Gynecol. 23(4), 439–452 (2009)CrossRefGoogle Scholar
  3. 3.
    Chi, J.G., Dooling, E.C., Gilles, F.H.: Gyral development of the human brain. Ann. Neurol. 1(1), 86–93 (1977)CrossRefGoogle Scholar
  4. 4.
    Good, C.D., Johnsrude, I.S., Ashburner, J., Henson, R.N., Friston, K.J., Frackowiak, R.S.: A voxel-based morphometric study of ageing in 465 normal adult human brains. NeuroImage 14(1 pt. 1), 21–36 (2001)Google Scholar
  5. 5.
    Thompson, P.M., Giedd, J.N., Woods, R.P., MacDonald, D., Evans, A.C., Toga, A.W.: Growth patterns in the developing brain detected by using continuum mechanical tensor maps. Nature 404(6774), 190–193 (2000)CrossRefGoogle Scholar
  6. 6.
    Franke, K., Luders, E., May, A., Wilke, M., Gaser, C.: Brain maturation: predicting individual BrainAGE in children and adolescents using structural MRI. NeuroImage 63(3), 1305–1312 (2012)CrossRefGoogle Scholar
  7. 7.
    Sabuncu, M.R., Van Leemput, K.: The relevance voxel machine (RVoxM): A self-tuning Bayesian model for informative image-based prediction. IEEE Trans Med Imaging 31(12), 2290–2306 (2012)CrossRefGoogle Scholar
  8. 8.
    Toews, M., William, W.3., Louis, C.D., Tal, A.: Feature-based morphometry: discovering group-related anatomical patterns. NeuroImage 49(3), 2318–2327 (2010)CrossRefGoogle Scholar
  9. 9.
    Toi, A., Lister, W.S., Fong, K.W.: How early are fetal cerebral sulci visible at prenatal ultrasound and what is the normal pattern of early fetal sulcal development? Ultrasound Obstet. Gynecol. 24(7), 706–715 (2004)CrossRefGoogle Scholar
  10. 10.
    Namburete, A.I.L., Stebbing, R.V., Noble, J.A.: Cranial parametrization of the fetal head for 3D ultrasound image analysis. In: MIUA, pp. 196–201 (2013)Google Scholar
  11. 11.
    Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)CrossRefzbMATHGoogle Scholar

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

Personalised recommendations