Journal article
Fully-automated alignment of 3D fetal brain ultrasound to a canonical reference space using multi-task learning.
- Abstract:
-
Methods for aligning 3D fetal neurosonography images must be robust to (i) intensity variations, (ii) anatomical and age-specific differences within the fetal population, and (iii) the variations in fetal posi- tion. To this end, we propose a multi-task fully convolutional neural network (FCN) architecture to ad- dress the problem of 3D fetal brain localization, structural segmentation, and alignment to a referential coordinate system. Instead of treating these tasks as independent problems, ...
Expand abstract
- Publication status:
- Published
- Peer review status:
- Peer reviewed
Actions
Access Document
- Files:
-
-
(Version of record, pdf, 3.9MB)
-
- Publisher copy:
- 10.1016/j.media.2018.02.006
Why is the content I wish to access not available via ORA?
Bibliographic data (the information relating to research outputs) and full-text items (e.g. articles, theses, reports, etc.) arrive in ORA from several different sources. Unfortunately we are not able to make available the full-text for every research output.
Please contact the ORA team (
) if you have queries regarding unavailable content OR if you are aware of a full-text copy we can make available.
) if you have queries regarding unavailable content OR if you are aware of a full-text copy we can make available.
Content may be unavailable for the following four reasons
-
Version unsuitable
-
We have not obtained a suitable full-text for a given research output. See this page for more information.
-
Recently completed
-
Sometimes content is held in ORA but is unavailable for a fixed period of time to comply with the policies and wishes of rights holders.
-
Permissions
-
All content made available in ORA should comply with relevant rights, such as copyright. See this page for more information.
-
Clearance
-
Some thesis volumes scanned as part of the digitisation scheme funded by Dr Leonard Polonsky are currently unavailable due to sensitive material or uncleared third-party copyright content. We are attempting to contact authors whose theses are affected.
Alternative access to the full-text
You may be able to access the full-text directly from the publisher's website using the 'Publisher Copy' link in the 'Links & Downloads' box from a research output's ORA record page. This method may require an institutional or individual subscription to the journal/resource.
Authors
Funding
Bill and Melinda Gates Foundation
More from this funder
Royal Academy of Engineering
More from this funder
Google
More from this funder
Innovate UK
More from this funder
Bibliographic Details
- Publisher:
- Elsevier Publisher's website
- Journal:
- Medical Image Analysis Journal website
- Volume:
- 46
- Pages:
- 1-14
- Publication date:
- 2018-02-01
- Acceptance date:
- 2018-02-19
- DOI:
- EISSN:
-
1361-8423
- ISSN:
-
1361-8415
- Pmid:
-
29499436
- Source identifiers:
-
827949
Item Description
- Language:
- English
- Keywords:
- Pubs id:
-
pubs:827949
- UUID:
-
uuid:a94562ac-effd-43b4-b723-958200d39695
- Local pid:
- pubs:827949
- Deposit date:
- 2018-03-14
Terms of use
- Copyright holder:
- Namburete et al
- Copyright date:
- 2018
- Notes:
- © 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)
If you are the owner of this record, you can report an update to it here: Report update to this record