Spatio-temporal partitioning and description of full-length routine fetal anomaly ultrasound scans
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. Th...
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Format: | Conference item |
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IEEE
2019
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author | Sharma, H Droste, R Chatelain, P Drukker, L Papageorghiou, A Noble, J |
author_facet | Sharma, H Droste, R Chatelain, P Drukker, L Papageorghiou, A Noble, J |
author_sort | Sharma, H |
collection | OXFORD |
description | 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. |
first_indexed | 2024-03-06T21:03:10Z |
format | Conference item |
id | oxford-uuid:3b8ec67e-c443-40ac-a2ef-e8da14fb5f62 |
institution | University of Oxford |
last_indexed | 2024-03-06T21:03:10Z |
publishDate | 2019 |
publisher | IEEE |
record_format | dspace |
spelling | oxford-uuid:3b8ec67e-c443-40ac-a2ef-e8da14fb5f622022-03-26T14:08:20ZSpatio-temporal partitioning and description of full-length routine fetal anomaly ultrasound scansConference itemhttp://purl.org/coar/resource_type/c_5794uuid:3b8ec67e-c443-40ac-a2ef-e8da14fb5f62Symplectic Elements at OxfordIEEE2019Sharma, HDroste, RChatelain, PDrukker, LPapageorghiou, ANoble, JThis 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. |
spellingShingle | Sharma, H Droste, R Chatelain, P Drukker, L Papageorghiou, A Noble, J Spatio-temporal partitioning and description of full-length routine fetal anomaly ultrasound scans |
title | Spatio-temporal partitioning and description of full-length routine fetal anomaly ultrasound scans |
title_full | Spatio-temporal partitioning and description of full-length routine fetal anomaly ultrasound scans |
title_fullStr | Spatio-temporal partitioning and description of full-length routine fetal anomaly ultrasound scans |
title_full_unstemmed | Spatio-temporal partitioning and description of full-length routine fetal anomaly ultrasound scans |
title_short | Spatio-temporal partitioning and description of full-length routine fetal anomaly ultrasound scans |
title_sort | spatio temporal partitioning and description of full length routine fetal anomaly ultrasound scans |
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