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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
Version:
Accepted Manuscript

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Institution:
University of Oxford
Department:
Oxford, Biomedical Imaging CDT
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Institution:
University of Oxford
Department:
Oxford, MSD, RDM
More by this author
Institution:
University of Oxford
Department:
Oxford, MPLS, Engineering Science
Publisher:
Springer International Publishing Publisher's website
Volume:
Lecture Notes in Computer Science: 9534
Pages:
180-189
Publication date:
2016
DOI:
EISSN:
1611-3349
ISSN:
0302-9743
URN:
uuid:fb67ab73-3004-4faf-87ec-0c405d252188
Source identifiers:
598716
Local pid:
pubs:598716
ISBN:
9783319287119

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