Automated description and workflow analysis of fetal echocardiography in first-trimester ultrasound video scans

This paper presents a novel, fully-automatic framework for fetal echocardiography analysis of full-length routine firsttrimester fetal ultrasound scan video. In this study, a new deep learning architecture, which considers spatio-temporal information and spatial attention, is designed to temporally...

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Main Authors: Yasrab, R, Alsharid, M, Sarker, MD, Zhao, H, Papageorghiou, A, Noble, J
Format: Conference item
Language:English
Published: IEEE 2023
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author Yasrab, R
Alsharid, M
Sarker, MD
Zhao, H
Papageorghiou, A
Noble, J
author_facet Yasrab, R
Alsharid, M
Sarker, MD
Zhao, H
Papageorghiou, A
Noble, J
author_sort Yasrab, R
collection OXFORD
description This paper presents a novel, fully-automatic framework for fetal echocardiography analysis of full-length routine firsttrimester fetal ultrasound scan video. In this study, a new deep learning architecture, which considers spatio-temporal information and spatial attention, is designed to temporally partition ultrasound video into semantically meaningful segments. The resulting automated semantic annotation is used to analyse cardiac examination workflow. The proposed 2D+t convolution neural network architecture achieves an A1 accuracy of 96.37%, F1 of 95.61%, and precision of 96.18% with 21.49% fewer parameters than the smallest ResNet-based architecture. Automated deep-learning based semantic annotation of unlabelled video scans (n=250) shows a high correlation with expert cardiac annotations (ρ = 0.96, p = 0.0004), thereby demonstrating the applicability of the proposed annotation model for echocardiography workflow analysis.
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spelling oxford-uuid:86d61a3a-8bb1-4bd4-8637-c03c7a7fb9cb2023-10-09T10:11:11ZAutomated description and workflow analysis of fetal echocardiography in first-trimester ultrasound video scansConference itemhttp://purl.org/coar/resource_type/c_5794uuid:86d61a3a-8bb1-4bd4-8637-c03c7a7fb9cbEnglishSymplectic ElementsIEEE2023Yasrab, RAlsharid, MSarker, MDZhao, HPapageorghiou, ANoble, JThis paper presents a novel, fully-automatic framework for fetal echocardiography analysis of full-length routine firsttrimester fetal ultrasound scan video. In this study, a new deep learning architecture, which considers spatio-temporal information and spatial attention, is designed to temporally partition ultrasound video into semantically meaningful segments. The resulting automated semantic annotation is used to analyse cardiac examination workflow. The proposed 2D+t convolution neural network architecture achieves an A1 accuracy of 96.37%, F1 of 95.61%, and precision of 96.18% with 21.49% fewer parameters than the smallest ResNet-based architecture. Automated deep-learning based semantic annotation of unlabelled video scans (n=250) shows a high correlation with expert cardiac annotations (ρ = 0.96, p = 0.0004), thereby demonstrating the applicability of the proposed annotation model for echocardiography workflow analysis.
spellingShingle Yasrab, R
Alsharid, M
Sarker, MD
Zhao, H
Papageorghiou, A
Noble, J
Automated description and workflow analysis of fetal echocardiography in first-trimester ultrasound video scans
title Automated description and workflow analysis of fetal echocardiography in first-trimester ultrasound video scans
title_full Automated description and workflow analysis of fetal echocardiography in first-trimester ultrasound video scans
title_fullStr Automated description and workflow analysis of fetal echocardiography in first-trimester ultrasound video scans
title_full_unstemmed Automated description and workflow analysis of fetal echocardiography in first-trimester ultrasound video scans
title_short Automated description and workflow analysis of fetal echocardiography in first-trimester ultrasound video scans
title_sort automated description and workflow analysis of fetal echocardiography in first trimester ultrasound video scans
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