Learning Ultrasound Scanning Skills from Human Demonstrations
This work addresses the problem of automating ultrasound scanning for medical applications, representing an incremental advancement in robotic ultrasound systems.
The paper tackles the challenge of modeling and transferring ultrasound scanning skills from physicians to robots or trainees by proposing a learning-based framework that encapsulates skills into a multimodal model from human demonstrations and uses a sampling-based strategy for guidance, validated with experiments on real data.
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.