Learning Ultrasound Scanning Skills from Human Demonstrations

TL;DR

This paper presents a learning-based framework that captures ultrasound scanning skills from expert demonstrations, enabling robots or novices to perform ultrasound procedures more effectively by modeling interactions among images, probe pose, and contact force.

Contribution

It introduces a multi-modal model of ultrasound skills learned from expert data and a sampling-based strategy to guide novices or robots during scanning.

Findings

Model effectively captures ultrasound skills from demonstrations

Guidance strategy improves scanning accuracy for novices

Framework validated with real sonographer data

Abstract

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.