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Ultrasound is progressing toward becoming an affordable and versatilesolution to medical imaging. With the advent of COVID-19 global pandemic, thereis a need to fully automate ultrasound imaging as it requires trained operatorsin close proximity to patients for a long period of time, therefore increasingrisk of infection. In this work, we investigate the important yetseldom-studied problem of scan target localization, under the setting of lungultrasound imaging. We propose a purely vision-based, data driven method thatincorporates learning-based computer vision techniques. We combine a human poseestimation model with a specially designed regression model to predict the lungultrasound scan targets, and deploy multiview stereo vision to enhance theconsistency of 3D target localization. While related works mostly focus onphantom experiments, we collect data from 30 human subjects for testing. Ourmethod attains an accuracy level of 16.00(9.79) mm for probe positioning and4.44(3.75) degree for probe orientation, with a success rate above 80% under anerror threshold of 25mm for all scan targets. Moreover, our approach can serveas a general solution to other types of ultrasound modalities. The code forimplementation has been released.

Localizing Scan Targets from Human Pose for Autonomous Lung Ultrasound Imaging