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Predicting fetal neurodevelopmental age from ultrasound images

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...

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Publication status:
Published
Peer review status:
Peer reviewed

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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
Nuffield Department of Women's and Reproductive Health
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Department of Engineering Science
Oxford college:
St Hilda's College
Role:
Author
ORCID:
0000-0002-3060-3772
Publisher:
Springer Publisher's website
Journal:
Lecture Notes in Computer Science: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014 Journal website
Pages:
260-267
Host title:
Lecture Notes in Computer Science: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014
Publication date:
2014-01-01
DOI:
ISSN:
0302-9743
Pmid:
25485387
Source identifiers:
485394
ISBN:
9783319104690
Keywords:
Pubs id:
pubs:485394
UUID:
uuid:6a63290a-e6b1-4e60-bb1f-2d282d8a5b73
Local pid:
pubs:485394
Deposit date:
2019-02-18

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