VeniBot: Towards Autonomous Venipuncture with Semi-supervised Vein Segmentation from Ultrasound Images

TL;DR

VeniBot is a semi-automated robotic system for venipuncture that combines novel hardware with a semi-supervised deep learning framework for vein segmentation from ultrasound images, improving accuracy and reducing failure rates.

Contribution

The paper introduces VeniBot, a new robotic system with a semi-supervised deep learning method for vein segmentation, enabling fully automated venipuncture.

Findings

Improved DSC by 5.36% over fully-supervised models

Reduced centroid error by 1.38 pixels

Lowered failure rate by 5.60%

Abstract

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.