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Journal article

Fully-automated alignment of 3D fetal brain ultrasound to a canonical reference space using multi-task learning.

Abstract:

Methods for aligning 3D fetal neurosonography images must be robust to (i) intensity variations, (ii) anatomical and age-specific differences within the fetal population, and (iii) the variations in fetal posi- tion. To this end, we propose a multi-task fully convolutional neural network (FCN) architecture to ad- dress the problem of 3D fetal brain localization, structural segmentation, and alignment to a referential coordinate system. Instead of treating these tasks as independent problems, ...

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Publication status:
Published
Peer review status:
Peer reviewed
Version:
Publisher's version

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Publisher copy:
10.1016/j.media.2018.02.006

Authors


More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author
Bill and Melinda Gates Foundation More from this funder
Royal Academy of Engineering More from this funder
Innovate UK More from this funder
Publisher:
Elsevier Publisher's website
Journal:
Medical Image Analysis Journal website
Volume:
46
Pages:
1-14
Publication date:
2018-02-05
Acceptance date:
2018-02-19
DOI:
EISSN:
1361-8423
ISSN:
1361-8415
Pubs id:
pubs:827949
URN:
uri:a94562ac-effd-43b4-b723-958200d39695
UUID:
uuid:a94562ac-effd-43b4-b723-958200d39695
Local pid:
pubs:827949

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