School of Information Science and Electrical Engineering, Shan Dong Jiao Tong University, Jinan 250000, China.
School of Control Science and Engineering, Shandong University, Jinan 250000, China.
Math Biosci Eng. 2022 May 30;19(8):7952-7977. doi: 10.3934/mbe.2022372.
Since the emergence of new coronaviruses and their variant virus, a large number of medical resources around the world have been put into treatment. In this case, the purpose of this article is to develop a handback intravenous intelligence injection robot, which reduces the direct contact between medical staff and patients and reduces the risk of infection. The core technology of hand back intravenous intelligent robot is a handlet venous vessel detection and segmentation and the position of the needle point position decision. In this paper, an image processing algorithm based on U-Net improvement mechanism (AT-U-Net) is proposed for core technology. It is investigated using a self-built dorsal hand vein database and the results show that it performs well, with an F1-score of 93.91%. After the detection of a dorsal hand vein, this paper proposes a location decision method for the needle entry point based on an improved pruning algorithm (PT-Pruning). The extraction of the trunk line of the dorsal hand vein is realized through this algorithm. Considering the vascular cross-sectional area and bending of each vein injection point area, the optimal injection point of the dorsal hand vein is obtained via a comprehensive decision-making process. Using the self-built dorsal hand vein injection point database, the accuracy of the detection of the effective injection area reaches 96.73%. The accuracy for the detection of the injection area at the optimal needle entry point is 96.50%, which lays a foundation for subsequent mechanical automatic injection.
自新型冠状病毒及其变异病毒出现以来，全球大量医疗资源投入治疗。在这种情况下，本文旨在开发一种手背静脉智能注射机器人，减少医护人员与患者的直接接触，降低感染风险。手背静脉智能机器人的核心技术是手静脉血管检测和分割以及针尖位置决策。本文针对核心技术提出了一种基于 U-Net 改进机制的图像处理算法（AT-U-Net）。使用自建的手背静脉数据库进行了研究，结果表明其性能良好，F1 得分为 93.91%。在手背静脉检测后，本文提出了一种基于改进剪枝算法（PT-Pruning）的针尖入口位置决策方法。通过该算法实现了手背静脉主干线的提取，考虑到每个静脉注射点区域的血管截面积和弯曲度，通过综合决策过程得到了手背静脉的最佳注射点。使用自建的手背静脉注射点数据库，有效注射区域的检测准确率达到 96.73%。最佳进针点注射区域的检测准确率为 96.50%，为后续机械自动注射奠定了基础。
