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SMOD - data augmentation based on statistical models of deformation to enhance segmentation in 2D cine cardiac MRI

Abstract:

Deep learning has revolutionized medical image analysis in recent years. Nevertheless, technical, ethical and financial constraints along with confidentiality issues still limit data availability, and therefore the performance of these approaches. To overcome such limitations, data augmentation has proven crucial. Here we propose SMOD, a novel augmentation methodology based on Statistical Models of Deformations, to segment 2D cine scans in cardiac MRI. In brief, the shape variability of the t...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1007/978-3-030-21949-9_39

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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:
Medical Sciences Division
Department:
RDM
Sub department:
RDM Cardiovascular Medicine
Oxford college:
Exeter College
Role:
Author
ORCID:
0000-0003-3214-671X
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author
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Publisher:
Springer Verlag Publisher's website
Journal:
Lecture Notes in Computer Science Journal website
Volume:
11504
Pages:
361-369
Host title:
Lecture Notes in Computer Science
Publication date:
2019-05-30
Acceptance date:
2019-05-15
DOI:
EISSN:
1611-3349
ISSN:
0302-9743
Source identifiers:
1020347
ISBN:
9783030219482
Keywords:
Pubs id:
pubs:1020347
UUID:
uuid:bcaa550f-f1cf-49d8-a41c-8e3e98380431
Local pid:
pubs:1020347
Deposit date:
2019-07-12

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