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A deep learning solution for automatic fetal neurosonographic diagnostic plane verification using clinical standard constraints

Abstract:

During routine ultrasound assessment of the fetal brain for biometry estimation and detection of fetal abnormalities, accurate imaging planes must be found by sonologists following a well-defined imaging protocol or clinical standard, which can be difficult for non-experts to do well. This assessment helps provide accurate biometry estimation and the detection of possible brain abnormalities. We describe a machine-learning method to assess automatically that transventricular ultrasound images...

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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:
Obstetrics & Gynaecology
Role:
Author
More by this author
Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
Obstetrics & Gynaecology
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Oxford college:
St Hilda's College
Role:
Author
Publisher:
Elsevier Publisher's website
Journal:
Ultrasound in Medicine and Biology Journal website
Volume:
43
Issue:
12
Pages:
2925-2933
Publication date:
2017-09-28
Acceptance date:
2017-07-17
DOI:
EISSN:
1879-291X
ISSN:
0301-5629
Pmid:
28958729
Source identifiers:
732807
Language:
English
Keywords:
Pubs id:
pubs:732807
UUID:
uuid:042475c3-ebff-43ce-aad0-58cd8c3df007
Local pid:
pubs:732807
Deposit date:
2017-12-16

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