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Learning to segment key clinical anatomical structures in fetal neurosonography informed by a region-based descriptor

Abstract:

We present a general framework for automatic segmentation of fetal brain structures in ultrasound images inspired by recent advances in machine learning. The approach is based on a region descriptor that characterizes the shape and local intensity context of different neurological structures without explicit models. To validate our framework, we present experiments to segment two fetal brain structures of clinical importance that have quite different ultrasonic appearances—the corpus callosum...

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Publication status:
Published
Peer review status:
Peer reviewed
Version:
Publisher's version

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Publisher copy:
10.1117/1.jmi.5.1.014007

Authors


More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
ORCID:
0000-0001-5672-6896
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
ORCID:
0000-0002-3060-3772
Royal Academy of Engineering More from this funder
Publisher:
Society of Photo-optical Instrumentation Engineers Publisher's website
Journal:
Journal of Medical Imaging Journal website
Volume:
5
Issue:
1
Pages:
014007
Publication date:
2018-03-10
Acceptance date:
2018-02-13
DOI:
EISSN:
2329-4310
ISSN:
2329-4302
Pubs id:
pubs:830321
URN:
uri:01331aa1-1e86-4f40-b15e-f5da8f9baa1c
UUID:
uuid:01331aa1-1e86-4f40-b15e-f5da8f9baa1c
Local pid:
pubs:830321

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