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Improving cardiac MRI convolutional neural network segmentation on small training datasets and dataset shift: A continuous kernel cut approach

Abstract:

Cardiac magnetic resonance imaging (MRI) provides a wealth of imaging biomarkers for cardiovascular disease care and segmentation of cardiac structures is required as a first step in enumerating these biomarkers. Deep convolutional neural networks (CNNs) have demonstrated remarkable success in image segmentation but typically require large training datasets and provide suboptimal results that require further improvements. Here, we developed a way to enhance cardiac MRI multi-class segmentatio...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1016/j.media.2020.101636

Authors


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Role:
Author
ORCID:
0000-0003-4622-5160
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Division:
MSD
Department:
RDM
Sub department:
RDM Cardiovascular Medicine
Role:
Author
ORCID:
0000-0002-0268-5221
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Publisher:
Elsevier Publisher's website
Journal:
Medical Image Analysis Journal website
Volume:
61
Article number:
101636
Publication date:
2020-01-11
Acceptance date:
2020-01-06
DOI:
EISSN:
1361-8423
ISSN:
1361-8415
Pmid:
31972427
Language:
English
Keywords:
Pubs id:
1084809
Local pid:
pubs:1084809
Deposit date:
2020-03-23

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