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Robust Regression of Brain Maturation from 3D Fetal Neurosonography Using CRNs

Part of the Lecture Notes in Computer Science book series (LNIP,volume 10554)


We propose a fully three-dimensional Convolutional Regression Network (CRN) for the task of predicting fetal brain maturation from 3D ultrasound (US) data. Anatomical development is modelled as the sonographic patterns visible in the brain at a given gestational age, which are aggregated by the model into a single value: the brain maturation (BM) score. These patterns are learned from 589 3D fetal volumes, and the model is applied to 3D US images of 146 fetal subjects acquired at multiple, ethnically diverse sites, spanning an age range of 18 to 36 gestational weeks. Achieving a mean error of 7.7 days between ground-truth and estimated maturational scores, our method outperforms the current state-of-art for automated BM estimation from 3D US images.


  • Brain Maturation (BM)
  • Fetal Neurosonography
  • Fetal Subjects
  • Random Forest Regression (RRF)
  • Huber Estimator

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  • DOI: 10.1007/978-3-319-67561-9_8
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    This result refers to the RRF model which exclusively used brain features, and did not incorporate information about fetal size (i.e. head circumference).


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Correspondence to Ana I. L. Namburete .

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Namburete, A.I.L., Xie, W., Noble, J.A. (2017). Robust Regression of Brain Maturation from 3D Fetal Neurosonography Using CRNs. In: , et al. Fetal, Infant and Ophthalmic Medical Image Analysis. OMIA FIFI 2017 2017. Lecture Notes in Computer Science(), vol 10554. Springer, Cham.

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