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Template-Free Estimation of Intracranial Volume: A Preterm Birth Animal Model Study Read chapter Read first chapter

2017 | OriginalPaper | Chapter

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

Authors : Ana I. L. Namburete, Weidi Xie, J. Alison Noble

Published in: Fetal, Infant and Ophthalmic Medical Image Analysis

Publisher: Springer International Publishing

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Abstract

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.

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previous chapter Multi-organ Detection in 3D Fetal Ultrasound with Machine Learning
next chapter Segmentation of Retinal Blood Vessels Using Dictionary Learning Techniques
Footnotes
1
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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Metadata
Title
Robust Regression of Brain Maturation from 3D Fetal Neurosonography Using CRNs
Authors
Ana I. L. Namburete
Weidi Xie
J. Alison Noble
Copyright Year
2017
Book
Fetal, Infant and Ophthalmic Medical Image Analysis

Print ISBN: 978-3-319-67560-2

Electronic ISBN: 978-3-319-67561-9

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-67561-9_8

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