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Automated characteriazation of the fetal heart in ultrasound images using fully convolutional neural networks

Abstract:

Automatic analysis of fetal echocardiography screening images could aid in the identification of congenital heart diseases. The first step towards automatic fetal echocardiography analysis is locating the fetal heart in an image and identifying the viewing (imaging) plane. This is highly challenging since the fetal heart is small with relatively indistinct anatomical structural appearance. This is further compounded by the presence of artefacts in ultrasound images. Herein we provide a state-...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1109/ISBI.2017.7950609

Authors


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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Sub department:
Institute of Biomedical Engineering
Oxford college:
Somerville College
Role:
Author
ORCID:
0000-0002-9451-4779
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Sub department:
Institute of Biomedical Engineering
Role:
Author
ORCID:
0000-0002-2242-351X
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Sub department:
Institute of Biomedical Engineering
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Sub department:
Institute of Biomedical Engineering
Role:
Author
ORCID:
0000-0002-3060-3772
Publisher:
IEEE
Host title:
2017 IEEE 14TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI 2017)
Journal:
Proceedings of the 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) More from this journal
Pages:
671-674
Publication date:
2017-06-17
Acceptance date:
2017-01-01
Event title:
2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)
Event location:
Melbourne, Australia
Event website:
https://biomedicalimaging.org/2017/
Event start date:
2017-04-18
Event end date:
2017-04-21
DOI:
EISSN:
1945-8452
ISSN:
1945-7928
ISBN:
9781509011711
Language:
English
Keywords:
Pubs id:
709252
Local pid:
pubs:709252
Deposit date:
2020-04-30

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