Gaze-Guided Robotic Vascular Ultrasound Leveraging Human Intention Estimation

Abstract

Medical ultrasound (US) has been widely used to examine vascular structure in modern clinical practice. However, the traditional US examination often faces challenges related to inter- and intra-operator variation. The robotic ultrasound system (RUSS) appears as a potential solution for such challenges because of its superiority in terms of stability and reproducibility. Given the complex anatomy of human vasculature, it is common for multiple vessels to appear in US images, or for a single vessel to bifurcate into multiple branches, complicating the examination process. To tackle this challenge, this work presents a gaze-guided RUSS for vascular applications. A gaze tracker is integrated to capture the eye movements of the human operator. The extracted gaze signal is utilized to guide the RUSS to follow the correct vessel when it bifurcates. Additionally, a gaze-guided segmentation network is proposed to enhance the segmentation robustness by exploiting the gaze information. However, gaze signals are often noisy, requiring interpretation to accurately discern the operator's true intentions. To this end, this study first proposed a stabilization module to process the raw gaze data. The inferred attention heatmap is then utilized as a region proposal to aid in segmentation and to serve as a trigger signal when the operator needs to adjust the scanning target, such as when a bifurcation appears in the current images. To ensure appropriate contact between the probe and the surface during the scanning, an automatic US confidence-based orientation correction method is developed as well. In the experiments, we demonstrated the efficiency of the proposed gaze-guided segmentation pipeline by comparing it with other segmentation methods. Besides, the performance of the proposed