  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 worldwide 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.
