VeniBot: Towards Autonomous Venipuncture with Semi-supervised Vein Segmentation from Ultrasound Images (2105.12945v1)

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.

• The paper introduces VeniBot, an autonomous venipuncture system that integrates innovative hardware with a semi-supervised deep learning framework for precise vein segmentation.
• It employs a semi-ResNeXt-Unet model with a mean teacher framework, achieving a 5.36% increase in DSC, reduced centroid error, and a lower failure rate.
• Validation on 40 volunteers demonstrates VeniBot's potential to reduce manual venipuncture risks, paving the way for safer and scalable medical robotics.

An Expert Overview of "VeniBot: Towards Autonomous Venipuncture with Semi-supervised Vein Segmentation from Ultrasound Images"

This paper presents "VeniBot," an innovative robotic system designed to perform autonomous venipuncture by integrating novel hardware and software. The research focuses on addressing the challenges of venipuncture, a common medical procedure that can be difficult for novices and risky for professionals due to potential needle injuries and infections. The proposed VeniBot system aims to automate this task more safely and efficiently, reducing the dependency on professional assistance.

Hardware Architecture of VeniBot

The hardware architecture of VeniBot consists of four primary units: supporting, positioning, puncturing, and imaging. The system's compact design integrates these units to facilitate full automation of the venipuncture process. The positioning and puncturing units are distinctive with their use of multiple degrees of freedom (DoF) enabled by DC motors and single-axis robots, which allow for precise navigation and manipulation. The imaging unit leverages near-infrared (NIR) and ultrasound sensors to achieve accurate vein detection and depth calculation, which are critical to the procedure's success.

Semi-supervised Vein Segmentation

A significant part of the VeniBot system's capabilities is the software component, particularly the novel semi-ResNeXt-Unet framework. This deep learning architecture is tailored for vein segmentation from ultrasound images, utilizing semi-supervised learning to enhance performance. Unlike fully supervised models that require extensive labeled datasets, semi-ResNeXt-Unet effectively leverages both labeled and unlabeled data. It does so by implementing a mean teacher learning framework that incorporates both supervised and consistency loss functions for network training.

The vein segmentation algorithm is benchmarked against various models, including pseudo label training and the $\rm{\Pi}$-model, displaying superior performance. Specifically, the semi-ResNeXt-Unet achieves a notable improvement of 5.36% in the Dice Similarity Coefficient (DSC), alongside a reduction in centroid error by 1.38 pixels and a decrease in failure rate by 5.60% compared to fully supervised approaches. These results underscore the robustness of the system in segmenting veins accurately, an essential factor for the successful navigation and operation of the VeniBot.

Validation and Implications

The VeniBot system was validated on a cohort of 40 volunteers, showing promising results in terms of both hardware accuracy and software precision. The integration of a semi-supervised learning framework in the VeniBot enhances its adaptability and performance across diverse datasets with minimal labeled input, offering potential scalability for broader medical applications.

The implications of this research are significant for the medical robotics field. The VeniBot system represents a step towards reducing the manual nature of venipuncture, minimizing the risk of human error, and enhancing procedural safety. The semi-supervised learning approach propels advancements in medical imaging, specifically ultrasound-based diagnostics, and could be further extended to other robotic-assisted medical procedures.

Future Directions

Future work should explore the integration of more advanced sensing technologies and optimization of VeniBot's components to enhance its precision and reliability further. Expanding the semi-supervised approach to incorporate multi-modal data could augment the system's capability to address cases with challenging vein accessibility. Continued validation and improvement of VeniBot's puncturing accuracy on diverse populations and settings will be crucial for its transition from a research prototype to a widely adopted clinical tool. Additionally, the adoption of the proposed semi-supervised framework in other AI-driven medical applications could catalyze developments across the medical technology landscape.

Ultimately, VeniBot lays the groundwork for a transformative step in seamlessly integrating AI with surgical robotics, heralding a new era of automated medical interventions driven by sophisticated machine learning algorithms.