BibTex

@InProceedings{10.1007/978-3-030-60334-2_18,
author="Wang, Yipei
and Droste, Richard
and Jiao, Jianbo
and Sharma, Harshita
and Drukker, Lior
and Papageorghiou, Aris T.
and Noble, J. Alison",
editor="Hu, Yipeng
and Licandro, Roxane
and Noble, J. Alison
and Hutter, Jana
and Aylward, Stephen
and Melbourne, Andrew
and Abaci Turk, Esra
and Torrents Barrena, Jordina",
title="Differentiating Operator Skill During Routine Fetal Ultrasound Scanning Using Probe Motion Tracking",
booktitle="Medical Ultrasound, and Preterm, Perinatal and Paediatric Image Analysis",
year="2020",
publisher="Springer International Publishing",
address="Cham",
pages="180--188",
abstract="In this paper, we consider differentiating operator skill during fetal ultrasound scanning using probe motion tracking. We present a novel convolutional neural network-based deep learning framework to model ultrasound probe motion in order to classify operator skill levels, that is invariant to operators' personal scanning styles. In this study, probe motion data during routine second-trimester fetal ultrasound scanning was acquired by operators of known experience levels (2 newly-qualified operators and 10 expert operators). The results demonstrate that the proposed model can successfully learn underlying probe motion features that distinguish operator skill levels during routine fetal ultrasound with 95{\%} accuracy.",
isbn="978-3-030-60334-2"
}