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.
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References
ISUOG: Sonographic examination of the fetal central nervous system. Ultrasound Obstet. Gynecol. 29(1), 109–116 (2007)
Bottomley, C., Bourne, T.: Dating and growth in the first trimester. Best Pract. Res. Cl Ostet. Gynecol. 23(4), 439–452 (2009)
Chi, J.G., Dooling, E.C., Gilles, F.H.: Gyral development of the human brain. Ann. Neurol. 1(1), 86–93 (1977)
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)
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)
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)
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)
Toews, M., William, W.3., Louis, C.D., Tal, A.: Feature-based morphometry: discovering group-related anatomical patterns. NeuroImage 49(3), 2318–2327 (2010)
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)
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)
Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)
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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
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