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Abdomen segmentation in 3D fetal ultrasound using CNN-powered deformable models

Abstract:

In this paper, voxel probability maps generated by a novel fovea fully convolutional network architecture (FovFCN) are used as additional feature images in the context of a segmentation approach based on deformable shape models. The method is applied to fetal 3D ultrasound image data aiming at a segmentation of the abdominal outline of the fetal torso. This is of interest, e.g., for measuring the fetal abdominal circumference, a standard biometric measure in prenatal screening. The method is ...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1007/978-3-319-67561-9_6

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Institution:
University of Oxford
Division:
Medical Sciences Division
Department:
Women's & Reproductive Health
Role:
Author
Publisher:
Springer
Host title:
International Workshop on Fetal and Infant Image Analysis/International Workshop on Ophthalmic Medical Image Analysis (FIFI 2017/OMIA 2017)
Journal:
International Workshop on Fetal and Infant Image Analysis/International Workshop on Ophthalmic Medical Image Analysis (FIFI 2017/OMIA 2017) More from this journal
Publication date:
2017-09-09
Acceptance date:
2017-05-16
DOI:
EISSN:
1611-3349
ISSN:
0302-9743
ISBN:
9783319675602
Pubs id:
pubs:735012
UUID:
uuid:0973b23c-6f7d-4322-a6f8-e1201391bb12
Local pid:
pubs:735012
Source identifiers:
735012
Deposit date:
2018-04-27

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