  In the modern medical care, venipuncture is an indispensable procedure for
both diagnosis and treatment. In this paper, unlike existing solutions that
fully or partially rely on professional assistance, we propose VeniBot -- a
compact robotic system solution integrating both novel hardware and software
developments. For the hardware, we design a set of units to facilitate the
supporting, positioning, puncturing and imaging functionalities. For the
software, to move towards a full automation, we propose a novel deep learning
framework -- semi-ResNeXt-Unet for semi-supervised vein segmentation from
ultrasound images. From which, the depth information of vein is calculated and
used to enable automated navigation for the puncturing unit. VeniBot is
validated on 40 volunteers, where ultrasound images can be collected
successfully. For the vein segmentation validation, 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.

Image and Video Processing