Conference item
Spatio-temporal partitioning and description of full-length routine fetal anomaly ultrasound scans
- Abstract:
- This paper considers automatic clinical workflow description of full-length routine fetal anomaly ultrasound scans using deep learning approaches for spatio-temporal video analysis. Multiple architectures consisting of 2D and 2D + t CNN, LSTM, and convolutional LSTM are investigated and compared. The contributions of short-term and long-term temporal changes are studied, and a multi-stream framework analysis is found to achieve the best top-l accuracy =0.77 and top-3 accuracy =0.94. Automated partitioning and characterisation on unlabelled full-length video scans show high correlation (ρ=0.95, p=0.0004) with workflow statistics of manually labelled videos, suggesting practicality of proposed methods.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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Access Document
- Files:
-
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(Preview, Accepted manuscript, pdf, 1.7MB, Terms of use)
-
- Publisher copy:
- 10.1109/ISBI.2019.8759149
Authors
- Publisher:
- IEEE
- Host title:
- IEEE International Symposium on Biomedical Imaging (ISBI 2019)
- Journal:
- IEEE International Symposium on Biomedical Imaging (ISBI) More from this journal
- Pages:
- 987-990
- Publication date:
- 2019-07-11
- Acceptance date:
- 2018-12-18
- DOI:
- EISSN:
-
1945-8452
- ISSN:
-
1945-7928
- ISBN:
- 9781538636428
- Pubs id:
-
pubs:965466
- UUID:
-
uuid:3b8ec67e-c443-40ac-a2ef-e8da14fb5f62
- Local pid:
-
pubs:965466
- Source identifiers:
-
965466
- Deposit date:
-
2019-01-25
Terms of use
- Copyright holder:
- IEEE
- Copyright date:
- 2019
- Notes:
- © 2019 IEEE. This paper was presented at the 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019). This is the accepted manuscript version of the article. The final version is available online from IEEE at: https://doi.org/10.1109/ISBI.2019.8759149
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