Conference item
Real-time prediction of segmentation quality
- Abstract:
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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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Access Document
- Files:
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(Accepted manuscript, pdf, 750.4KB)
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- Publisher copy:
- 10.1007/978-3-030-00937-3_66
Authors
Funding
Bibliographic Details
- Publisher:
- Springer Publisher's website
- Host title:
- 21st International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2018)
- Journal:
- 21st International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2018) Journal website
- Publication date:
- 2018-09-13
- Acceptance date:
- 2018-05-25
- DOI:
- EISSN:
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1611-3349
- ISSN:
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0302-9743
- ISBN:
- 9783030009366
Item Description
- Pubs id:
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pubs:923330
- UUID:
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uuid:46b5f873-d3f4-4bc5-9a88-5d80eef52524
- Local pid:
- pubs:923330
- Source identifiers:
-
923330
- Deposit date:
- 2018-10-18
Terms of use
- Copyright holder:
- Springer Nature Switzerland AG
- Copyright date:
- 2018
- Notes:
- © Springer Nature Switzerland AG 2018. This is the accepted manuscript version of the paper. The final version is available online from Springer at: https://doi.org/10.1007/978-3-030-00937-3_66
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