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TVnet: automated time-resolved tracking of the tricuspid valve plane in MRI long-axis cine images with a dual-stage deep learning pipeline

Abstract:

Tracking the tricuspid valve (TV) in magnetic resonance imaging (MRI) long-axis cine images has the potential to aid in the evaluation of right ventricular dysfunction, which is common in congenital heart disease and pulmonary hypertension. However, this annotation task remains difficult and time-demanding as the TV moves rapidly and is barely distinguishable from the myocardium. This study presents TVnet, a novel dual-stage deep learning pipeline based on ResNet-50 and automated image linear...

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Publication status:
Published
Peer review status:
Peer reviewed

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Institution:
University of Oxford
Division:
MSD
Department:
RDM
Sub department:
RDM Cardiovascular Medicine
Oxford college:
Balliol College
Role:
Author
ORCID:
0000-0002-9384-4602
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Role:
Author
ORCID:
0000-0003-1931-2971
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Role:
Author
ORCID:
0000-0003-3074-5380
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Role:
Author
ORCID:
0000-0003-2848-3326
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Role:
Author
ORCID:
0000-0002-9432-0448
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Publisher:
Springer Publisher's website
Pages:
567-576
Series:
Lecture Notes in Computer Science
Series number:
12906
Host title:
Medical Image Computing and Computer Assisted Intervention – MICCAI 2021
Publication date:
2021-09-21
Acceptance date:
2021-06-11
DOI:
EISBN:
9783030872311
EISSN:
1611-3349
ISSN:
0302-9743
ISBN:
9783030872304
Language:
English
Keywords:
Subtype:
Chapter
Pubs id:
1196027
Local pid:
pubs:1196027
Deposit date:
2021-09-27

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