- 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
- Version:
- Publisher's version
- Files:
-
-
(pdf, 3.9MB)
-
- Publisher copy:
- 10.1016/j.media.2018.02.006
-
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- Publisher:
- Elsevier Publisher's website
- Journal:
- Medical Image Analysis Journal website
- Volume:
- 46
- Pages:
- 1-14
- Publication date:
- 2018-02-05
- Acceptance date:
- 2018-02-19
- DOI:
- EISSN:
-
1361-8423
- ISSN:
-
1361-8415
- Pubs id:
-
pubs:827949
- URN:
-
uri:a94562ac-effd-43b4-b723-958200d39695
- UUID:
-
uuid:a94562ac-effd-43b4-b723-958200d39695
- Local pid:
- pubs:827949
- Language:
- English
- Keywords:
- 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/)
Journal article
Fully-automated alignment of 3D fetal brain ultrasound to a canonical reference space using multi-task learning.
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Bill and Melinda Gates Foundation
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Royal Academy of Engineering
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Google
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Innovate UK
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