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Robust regression of brain maturation from 3D fetal neurosonography using CRNs

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, et...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1007/978-3-319-67561-9_8

Authors


More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Oxford college:
St Hildas College
ORCID:
0000-0002-3060-3772
Publisher:
Springer, Cham Publisher's website
Volume:
10554
Pages:
73-80
Series:
Lecture Notes in Computer Science
Publication date:
2017-09-09
Acceptance date:
2017-07-08
DOI:
ISSN:
0302-9743
Pubs id:
pubs:735123
URN:
uri:45acfec8-2eaa-4ebc-8ce3-f19cc7f95cb9
UUID:
uuid:45acfec8-2eaa-4ebc-8ce3-f19cc7f95cb9
Local pid:
pubs:735123
ISBN:
9783319675602

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