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Predicting Fetal Neurodevelopmental Age from Ultrasound Images

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNIP,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. ISUOG: Sonographic examination of the fetal central nervous system. Ultrasound Obstet. Gynecol. 29(1), 109–116 (2007)

    Google Scholar 

  2. Bottomley, C., Bourne, T.: Dating and growth in the first trimester. Best Pract. Res. Cl Ostet. Gynecol. 23(4), 439–452 (2009)

    CrossRef  Google Scholar 

  3. Chi, J.G., Dooling, E.C., Gilles, F.H.: Gyral development of the human brain. Ann. Neurol. 1(1), 86–93 (1977)

    CrossRef  Google Scholar 

  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. 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)

    CrossRef  Google Scholar 

  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)

    CrossRef  Google Scholar 

  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)

    CrossRef  Google Scholar 

  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)

    CrossRef  Google Scholar 

  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)

    CrossRef  Google Scholar 

  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. Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)

    CrossRef  MATH  Google Scholar 

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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. https://doi.org/10.1007/978-3-319-10470-6_33

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  • DOI: https://doi.org/10.1007/978-3-319-10470-6_33

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-10469-0

  • Online ISBN: 978-3-319-10470-6

  • eBook Packages: Computer ScienceComputer Science (R0)