Droste Richard, Drukker Lior, Papageorghiou Aris T, Noble J Alison
Institute of Biomedical Engineering, University of Oxford, Oxford, UK.
Nuffield Department of Womens & Reproductive Health, University of Oxford, Oxford, UK.
Med Image Comput Comput Assist Interv. 2020 Oct;12263:583-592. doi: 10.1007/978-3-030-59716-0_56. Epub 2020 Sep 29.
We present the first system that provides real-time probe movement guidance for acquiring standard planes in routine freehand obstetric ultrasound scanning. Such a system can contribute to the world-wide deployment of obstetric ultrasound scanning by lowering the required level of operator expertise. The system employs an artificial neural network that receives the ultrasound video signal and the motion signal of an inertial measurement unit (IMU) that is attached to the probe, and predicts a guidance signal. The network termed US-GuideNet predicts either the movement towards the standard plane position (goal prediction), or the next movement that an expert sonographer would perform (action prediction). While existing models for other ultrasound applications are trained with simulations or phantoms, we train our model with real-world ultrasound video and probe motion data from 464 routine clinical scans by 17 accredited sonographers. Evaluations for 3 standard plane types show that the model provides a useful guidance signal with an accuracy of 88.8 % for goal prediction and 90.9 % for action prediction.
我们展示了首个在常规徒手产科超声扫描中为获取标准平面提供实时探头移动引导的系统。这样一个系统可以通过降低所需的操作员专业水平，助力产科超声扫描在全球范围内的推广。该系统采用一个人工神经网络，它接收超声视频信号以及附着在探头上的惯性测量单元（IMU）的运动信号，并预测一个引导信号。名为US - GuideNet的网络可预测朝着标准平面位置的移动（目标预测），或者专家超声医师接下来会执行的移动（动作预测）。虽然其他超声应用的现有模型是通过模拟或体模进行训练的，但我们使用来自17名经认可的超声医师的464次常规临床扫描的真实世界超声视频和探头运动数据来训练我们的模型。对3种标准平面类型的评估表明，该模型提供了一个有用的引导信号，目标预测的准确率为88.8%，动作预测的准确率为90.9%。
