Automatic Probe Movement Guidance for Freehand Obstetric Ultrasound

TL;DR

This paper introduces US-GuideNet, a real-time neural network system that guides probe movement in obstetric ultrasound, aiming to make scans more accessible and reduce operator skill requirements.

Contribution

The paper presents the first real-time guidance system trained on real clinical data for obstetric ultrasound, improving standard plane acquisition accuracy.

Findings

Achieved 88.8% accuracy in goal prediction.

Achieved 90.9% accuracy in action prediction.

Trained on data from 464 clinical scans by 17 sonographers.

Abstract

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.