@inproceedings{ecced1b5bf6743f29a3f8fc6fa5ddba5,
title = "Automated Description and Workflow Analysis of Fetal Echocardiography in First-Trimester Ultrasound Video Scans",
abstract = "This paper presents a novel, fully-automatic framework for fetal echocardiography analysis of full-length routine first-trimester 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.",
keywords = "echocardiography, fetal heart, first trimester, spatio-temporal analysis, ultrasound",
author = "Robail Yasrab and Mohammad Alsharid and Sarker, \{Md Mostafa Kamal\} and He Zhao and Papageorghiou, \{Aris T.\} and Noble, \{J. Alison\}",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 20th IEEE International Symposium on Biomedical Imaging, ISBI 2023 ; Conference date: 18-04-2023 Through 21-04-2023",
year = "2023",
doi = "10.1109/ISBI53787.2023.10230422",
language = "British English",
series = "Proceedings - International Symposium on Biomedical Imaging",
publisher = "IEEE Computer Society",
booktitle = "2023 IEEE International Symposium on Biomedical Imaging, ISBI 2023",
address = "United States",
}