Semi-supervised Vein Segmentation of Ultrasound Images for Autonomous Venipuncture

Venipuncture is an indispensable procedure for both diagnosis and treatment. In this paper, unlike existing solutions that fully or partially rely on professional assistance, a compact robotic system integrating both novel hardware and software developments is introduced. The hardware consists of a set of units to facilitate the supporting, positioning, puncturing, and imaging functionalities. To achieve full automation, a novel deep learning framework - semi-ResNeXt-Unet for semi-supervised vein segmentation from ultrasound images is proposed. The depth information of vein is calculated and enables the automated navigation for the puncturing unit. The algorithm is validated on 40 volunteers, and the proposed semi-ResNeXt-Unet improves the dice similarity coefficient (DSC) by 5.36%, decreases the centroid error by 1.38 pixels and decreases the failure rate by 5.60%, compared to fully-supervised ResNeXt-Unet.