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Quality control-driven image segmentation towards reliable automatic image analysis in large-scale cardiovascular magnetic resonance aortic cine imaging

Abstract:

Recent progress in fully-automated image segmentation has enabled efficient extraction of clinical parameters in large-scale clinical imaging studies, reducing laborious manual processing. However, the current state-of-the-art automatic image segmentation may still fail, especially when it comes to atypical cases. Visual inspection of segmentation quality is often required, thus diminishing the improvements in efficiency. This drives an increasing need to enhance the overall data processing p...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1007/978-3-030-32245-8_83

Authors


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Institution:
University of Oxford
Division:
MSD
Department:
RDM
Sub department:
RDM Cardiovascular Medicine
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MSD
Department:
RDM
Sub department:
RDM Cardiovascular Medicine
Role:
Author
ORCID:
0000-0002-0452-8756
More by this author
Institution:
University of Oxford
Division:
MSD
Department:
RDM
Sub department:
RDM Cardiovascular Medicine
Role:
Author
ORCID:
0000-0003-3385-1242
More by this author
Institution:
University of Oxford
Division:
MSD
Department:
RDM
Sub department:
RDM Cardiovascular Medicine
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MSD
Department:
RDM
Sub department:
RDM Cardiovascular Medicine
Role:
Author
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Publisher:
Springer Publisher's website
Journal:
Lecture Notes in Computer Science Journal website
Volume:
11765
Pages:
750-758
Host title:
Medical Image Computing and Computer Assisted Intervention – MICCAI 2019
Publication date:
2019-10-10
Acceptance date:
2019-06-05
Event title:
22nd International Conference of Medical Image Computing and Computer Assisted Intervention (MICCAI 2019)
Event location:
Shenzhen, China
Event website:
https://www.miccai2019.org/
Event start date:
2019-10-13T00:00:00Z
Event end date:
2019-10-17T00:00:00Z
DOI:
EISBN:
9783030322458
EISSN:
1611-3349
ISSN:
0302-9743
ISBN:
9783030322441
Language:
English
Keywords:
Pubs id:
1077227
Local pid:
pubs:1077227
Deposit date:
2020-03-23

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