Wang Yipei, Droste Richard, Jiao Jianbo, Sharma Harshita, Drukker Lior, Papageorghiou Aris T, Noble J Alison
Institute of Biomedical Engineering, University of Oxford, Oxford, UK.
Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, UK.
Med Ultrasound Preterm Perinat Paediatr Image Anal (2020). 2020 Oct;12437:180-188. doi: 10.1007/978-3-030-60334-2_18. Epub 2020 Oct 1.
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.
在本文中，我们考虑使用探头运动跟踪来区分胎儿超声扫描过程中的操作者技能。我们提出了一种基于卷积神经网络的新型深度学习框架，用于对超声探头运动进行建模，以对操作者技能水平进行分类，该框架对操作者的个人扫描风格具有不变性。在本研究中，由具有已知经验水平的操作者（2名新合格操作者和10名专家操作者）采集了常规孕中期胎儿超声扫描过程中的探头运动数据。结果表明，所提出的模型能够成功学习到区分常规胎儿超声检查中操作者技能水平的潜在探头运动特征，准确率达95%。
