# Learning Ultrasound Scanning Skills from Human Demonstrations

arXiv: https://arxiv.org/abs/2111.09739 · HTML: https://mlsanity.com/paper/2111.09739/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 52 references that are in the map.
- First posted on arXiv 9 Nov 2021.

## Builds on (most cited first, 5 of 5)

- [Making Sense of Vision and Touch: Self-Supervised Learning of Multimodal Representations for Contact-Rich Tasks](https://mlsanity.com/paper/1810.10191/) (2018)
- [Towards MRI-Based Autonomous Robotic US Acquisitions: A First Feasibility Study](https://mlsanity.com/paper/1607.08371/) (2016)
- [Automatic Probe Movement Guidance for Freehand Obstetric Ultrasound](https://mlsanity.com/paper/2007.04480/) (2020)
- [Autonomous Navigation of an Ultrasound Probe Towards Standard Scan Planes with Deep Reinforcement Learning](https://mlsanity.com/paper/2103.00718/) (2021)
- [Planning for Multi-stage Forceful Manipulation](https://mlsanity.com/paper/2101.02679/) (2021)

## Built on by (most cited first, 2 of 2)

- [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)

## Abstract (arXiv)

Recently, the robotic ultrasound system has become an emerging topic owing to the widespread use of medical ultrasound. However, it is still a challenging task to model and to transfer the ultrasound skill from an ultrasound physician. In this paper, we propose a learning-based framework to acquire ultrasound scanning skills from human demonstrations. First, the ultrasound scanning skills are encapsulated into a high-dimensional multi-modal model in terms of interactions among ultrasound images, the probe pose and the contact force. The parameters of the model are learned using the data collected from skilled sonographers' demonstrations. Second, a sampling-based strategy is proposed with the learned model to adjust the extracorporeal ultrasound scanning process to guide a newbie sonographer or a robot arm. Finally, the robustness of the proposed framework is validated with the experiments on real data from sonographers.