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Journal article

Weakly-supervised convolutional neural networks for multimodal image registration

Abstract:

One of the fundamental challenges in supervised learning for multimodal image registration is the lack of ground-truth for voxel-level spatial correspondence. This work describes a method to infer voxel-level transformation from higher-level correspondence information contained in anatomical labels. We argue that such labels are more reliable and practical to obtain for reference sets of image pairs than voxel-level correspondence. Typical anatomical labels of interest may include solid organ...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1016/j.media.2018.07.002

Authors


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Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author
ORCID:
0000-0003-4902-0486
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Role:
Author
ORCID:
0000-0001-9207-7280
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Role:
Author
ORCID:
0000-0003-1081-2830
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Funding agency for:
Hu, Y
Grant:
CMIC Platform Fellowship EP/M020533/1
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Funding agency for:
Hu, Y
Grant:
CMIC Platform Fellowship EP/M020533/1
More from this funder
Funding agency for:
Hu, Y
Grant:
CMIC Platform Fellowship EP/M020533/1
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Publisher:
Elsevier Publisher's website
Journal:
Medical Image Analysis Journal website
Volume:
49
Pages:
1-13
Publication date:
2018-07-04
Acceptance date:
2018-07-03
DOI:
EISSN:
1361-8423
ISSN:
1361-8415
Pmid:
30007253
Source identifiers:
883178
Language:
English
Keywords:
Pubs id:
pubs:883178
UUID:
uuid:729810a6-f78c-44fd-84b1-d9769f7440f7
Local pid:
pubs:883178
Deposit date:
2018-09-14

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