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Real-time prediction of segmentation quality

Abstract:

Recent advances in deep learning based image segmentation methods have enabled real-time performance with human-level accuracy. However, occasionally even the best method fails due to low image quality, artifacts or unexpected behaviour of black box algorithms. Being able to predict segmentation quality in the absence of ground truth is of paramount importance in clinical practice, but also in large-scale studies to avoid the inclusion of invalid data in subsequent analysis. In this work, we ...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1007/978-3-030-00937-3_66

Authors


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GlaxoSmithKline More from this funder
Medical College of Saint Bartholomews Hospital More from this funder
Indonesia Endowment for Education More from this funder
National Institute for Health Research More from this funder
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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:
2018-09-13
Acceptance date:
2018-05-25
DOI:
EISSN:
1611-3349
ISSN:
0302-9743
Source identifiers:
923330
ISBN:
9783030009366
Pubs id:
pubs:923330
UUID:
uuid:46b5f873-d3f4-4bc5-9a88-5d80eef52524
Local pid:
pubs:923330
Deposit date:
2018-10-18

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