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Myocardial infarction detection from left ventricular shapes using a random forest

Abstract:

Understanding myocardial remodelling, and developing tools for its accurate quantification, is fundamental for improving the diagnosis and treatment of myocardial infarction patients. Conventional clinical metrics, such as blood pool volume or ejection fraction, are not always distinctive. Here we describe a method for the classification of myocardial infarction from 3D diastolic and systolic left ventricle shapes, represented by point sets. Classification features included global geometric, ...

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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
Sub department:
Institute of Biomedical Engineering
Role:
Author
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Institution:
University of Oxford
Division:
MSD
Department:
RDM
Role:
Author
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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
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Funding agency for:
Grau, V
Grant:
New Horizon Grant (NH/13/30238
More from this funder
Funding agency for:
Grau, V
Grant:
New Horizon Grant (NH/13/30238
Publisher:
Springer International Publishing Publisher's website
Volume:
Lecture Notes in Computer Science: 9534
Pages:
180-189
Host title:
Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges : 6th International Workshop, STACOM 2015, Held in Conjunction with MICCAI 2015, Munich, Germany, October 9, 2015, Revised Selected Papers
Publication date:
2016-01-01
DOI:
EISSN:
1611-3349
ISSN:
0302-9743
Source identifiers:
598716
ISBN:
9783319287119
Keywords:
Pubs id:
pubs:598716
UUID:
uuid:fb67ab73-3004-4faf-87ec-0c405d252188
Local pid:
pubs:598716
Deposit date:
2016-04-08

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