Conference item icon

Conference item

Joint learning of motion estimation and segmentation for cardiac MR image sequences

Abstract:

Cardiac motion estimation and segmentation play important roles in quantitatively assessing cardiac function and diagnosing cardiovascular diseases. In this paper, we propose a novel deep learning method for joint estimation of motion and segmentation from cardiac MR image sequences. The proposed network consists of two branches: a cardiac motion estimation branch which is built on a novel unsupervised Siamese style recurrent spatial transformer network, and a cardiac segmentation branch that...

Expand abstract
Publication status:
Published
Peer review status:
Peer reviewed

Actions


Access Document


Files:
Publisher copy:
10.1007/978-3-030-00934-2_53

Authors


More by this author
Institution:
University of Oxford
Division:
MSD
Department:
RDM
Sub department:
RDM Cardiovascular Medicine
Role:
Author
ORCID:
0000-0002-0268-5221
Expand authors...
Publisher:
Springer Publisher's website
Journal:
21st International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2018) Journal website
Host title:
21st International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2018)
Publication date:
2018-09-26
Acceptance date:
2018-05-25
DOI:
EISSN:
1611-3349
ISSN:
0302-9743
Source identifiers:
926698
ISBN:
9783030009335
Pubs id:
pubs:926698
UUID:
uuid:4270983f-02f4-4fcb-8a1a-af504962016d
Local pid:
pubs:926698
Deposit date:
2018-10-18

Terms of use


Views and Downloads






If you are the owner of this record, you can report an update to it here: Report update to this record

TO TOP