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Ω-Net (Omega-Net): Fully automatic, multi-view cardiac MR detection, orientation, and segmentation with deep neural networks

Abstract:

Pixelwise segmentation of the left ventricular (LV) myocardium and the four cardiac chambers in 2-D steady state free precession (SSFP) cine sequences is an essential preprocessing step for a wide range of analyses. Variability in contrast, appearance, orientation, and placement of the heart between patients, clinical views, scanners, and protocols makes fully automatic semantic segmentation a notoriously difficult problem. Here, we present Ω-Net (Omega-Net): A novel convolutional neural netw...

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Publication status:
Published
Peer review status:
Peer reviewed
Version:
Accepted Manuscript

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Publisher copy:
10.1016/j.media.2018.05.008

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Institution:
University of Oxford
Division:
Maths, Physical and Life Sciences
Department:
Engineering Science
More by this author
Institution:
University of Oxford
Division:
Maths, Physical and Life Sciences
Department:
Engineering Science
Bluemke, DA More by this author
More by this author
Institution:
University of Oxford
Division:
Maths, Physical and Life Sciences
Department:
Engineering Science
Publisher:
Elsevier Publisher's website
Journal:
Medical Image Analysis Journal website
Volume:
48
Pages:
95-106
Publication date:
2018-05-22
Acceptance date:
2018-05-17
DOI:
EISSN:
1361-8423
ISSN:
1361-8415
Pubs id:
pubs:856281
URN:
uri:fb440db7-2258-4f8e-b5c9-9a45335b39fb
UUID:
uuid:fb440db7-2258-4f8e-b5c9-9a45335b39fb
Local pid:
pubs:856281

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