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

arXiv: https://arxiv.org/abs/2105.12945 · HTML: https://mlsanity.com/paper/2105.12945/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 May 2021.

## Built on by (most cited first, 2 of 2)

- [A New Semi-supervised Learning Benchmark for Classifying View and Diagnosing Aortic Stenosis from Echocardiograms](https://mlsanity.com/paper/2108.00080/) (2021)
- [Edge AI-Based Vein Detector for Efficient Venipuncture in the Antecubital Fossa](https://mlsanity.com/paper/2310.18234/) (2023)

## Abstract (arXiv)

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.