Journal article
Weakly-supervised convolutional neural networks for multimodal image registration
- Abstract:
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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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(Version of record, pdf, 3.5MB)
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- Publisher copy:
- 10.1016/j.media.2018.07.002
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Authors
Funding
+ Engineering and Physical Sciences Research Council
More from this funder
Funding agency for:
Hu, Y
Grant:
CMIC Platform Fellowship EP/M020533/1
+ Cancer Research UK
More from this funder
Funding agency for:
Hu, Y
Grant:
CMIC Platform Fellowship EP/M020533/1
+ UCL-KCL Comprehensive Cancer Imaging Centre
More from this funder
Funding agency for:
Hu, Y
Grant:
CMIC Platform Fellowship EP/M020533/1
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Bibliographic Details
- 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:
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1361-8423
- ISSN:
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1361-8415
- Pmid:
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30007253
- Source identifiers:
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883178
Item Description
- Language:
- English
- Keywords:
- Pubs id:
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pubs:883178
- UUID:
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uuid:729810a6-f78c-44fd-84b1-d9769f7440f7
- Local pid:
- pubs:883178
- Deposit date:
- 2018-09-14
Terms of use
- Copyright holder:
- Hu et al
- Copyright date:
- 2018
- Notes:
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Copyright © 2018 The Authors. Published by Elsevier B.V.
This is an open access article under the CC BY license. (http://creativecommons.org/licenses/by/4.0/)
- Licence:
- CC Attribution (CC BY)
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