# Automatic Probe Movement Guidance for Freehand Obstetric Ultrasound

arXiv: https://arxiv.org/abs/2007.04480 · HTML: https://mlsanity.com/paper/2007.04480/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jul 2020.

## Built on by (most cited first, 28 of 28)

- [Autonomous Navigation of an Ultrasound Probe Towards Standard Scan Planes with Deep Reinforcement Learning](https://mlsanity.com/paper/2103.00718/) (2021)
- [Cardiac Copilot: Automatic Probe Guidance for Echocardiography with World Model](https://mlsanity.com/paper/2406.13165/) (2024)
- [EchoWorld: Learning Motion-Aware World Models for Echocardiography Probe Guidance](https://mlsanity.com/paper/2504.13065/) (2025)
- [AutoFB: Automating Fetal Biometry Estimation from Standard Ultrasound Planes](https://mlsanity.com/paper/2107.05255/) (2021)
- [Intelligent Robotic Sonographer: Mutual Information-based Disentangled Reward Learning from Few Demonstrations](https://mlsanity.com/paper/2307.03705/) (2023)
- [VesNet-RL: Simulation-based Reinforcement Learning for Real-World US Probe Navigation](https://mlsanity.com/paper/2205.06676/) (2022)
- [Structure-aware World Model for Probe Guidance via Large-scale Self-supervised Pre-train](https://mlsanity.com/paper/2406.19756/) (2024)
- [Image-Guided Navigation of a Robotic Ultrasound Probe for Autonomous Spinal Sonography Using a Shadow-aware Dual-Agent Framework](https://mlsanity.com/paper/2111.02167/) (2021)
- [Adaptive 3D Localization of 2D Freehand Ultrasound Brain Images](https://mlsanity.com/paper/2209.05477/) (2022)
- [Goal-conditioned reinforcement learning for ultrasound navigation guidance](https://mlsanity.com/paper/2405.01409/) (2024)
- [Learning Robotic Ultrasound Scanning Skills via Human Demonstrations and Guided Explorations](https://mlsanity.com/paper/2111.01625/) (2021)
- [Multi-Atlas Segmentation and Spatial Alignment of the Human Embryo in First Trimester 3D Ultrasound](https://mlsanity.com/paper/2202.06599/) (2022)
- [Multimodal-GuideNet: Gaze-Probe Bidirectional Guidance in Obstetric Ultrasound Scanning](https://mlsanity.com/paper/2207.12833/) (2022)
- [Ultrasound Plane Pose Regression: Assessing Generalized Pose Coordinates in the Fetal Brain](https://mlsanity.com/paper/2301.08317/) (2023)
- [UltraSeP: Sequence-aware Pre-training for Echocardiography Probe Movement Guidance](https://mlsanity.com/paper/2408.15026/) (2024)
- [Learning Ultrasound Scanning Skills from Human Demonstrations](https://mlsanity.com/paper/2111.09739/) (2021)
- [HUP-3D: A 3D multi-view synthetic dataset for assisted-egocentric hand-ultrasound pose estimation](https://mlsanity.com/paper/2407.09215/) (2024)
- [Pose-GuideNet: Automatic Scanning Guidance for Fetal Head Ultrasound from Pose Estimation](https://mlsanity.com/paper/2408.09931/) (2024)
- [VA-Adapter: Adapting Ultrasound Foundation Model to Echocardiography Probe Guidance](https://mlsanity.com/paper/2510.06809/) (2025)
- [Echo-POSED: Geometric Self-Distillation for Echocardiography Guidance](https://mlsanity.com/paper/2606.02634/) (2026)
- [Towards a Simple Framework of Skill Transfer Learning for Robotic Ultrasound-guidance Procedures](https://mlsanity.com/paper/2305.04004/) (2023)
- [Learning Autonomous Ultrasound via Latent Task Representation and Robotic Skills Adaptation](https://mlsanity.com/paper/2307.13323/) (2023)
- [Measuring proximity to standard planes during fetal brain ultrasound scanning](https://mlsanity.com/paper/2404.07124/) (2024)
- [Regional quality estimation for echocardiography using deep learning](https://mlsanity.com/paper/2408.00591/) (2024)
- [Uncertainty-Calibrated Explainable Artificial Intelligence for Fetal Ultrasound Plane Classification: A Systematic Review](https://mlsanity.com/paper/2601.00990/) (2026)
- [UltraStar: Semantic-Aware Star Graph Modeling for Echocardiography Navigation](https://mlsanity.com/paper/2603.01461/) (2026)
- [SAMe: A Semantic Anatomy Mapping Engine for Robotic Ultrasound](https://mlsanity.com/paper/2604.25646/) (2026)
- [Ultrasound Operator Guidance Using World Modeling and Retrieval Based Action Planning](https://mlsanity.com/paper/2610.06008/) (2026)

## Abstract (arXiv)

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.