2021/05/27 by Yu Chen, Chen, Yu, Yuxuan Wang +17
Engineering · Health Professions · Medicine · #Central Venous Catheters and Hemodialysis #Diagnosis and Treatment of Venous Diseases #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Venous Thromboembolism Diagnosis and Management #eess.IV #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2105.12945
arxiv created 2021/05/27 · openalex publication_date 2021/05/27 · arxiv updated 2021/05/28 · openalex created_date 2021/06/07 · openalex updated_date 2026/07/28
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.