Determination of the location of the needle entry point based on an improved pruning algorithm

Guangyuan Zhang; Xiaonan Gao; Zhenfang Zhu; Fengyv Zhou; Dexin Yu; Guangyuan Zhang; Xiaonan Gao; Zhenfang Zhu; Fengyv Zhou; Dexin Yu

doi:10.3934/mbe.2022372

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 8: 7952-7977. doi: 10.3934/mbe.2022372

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Determination of the location of the needle entry point based on an improved pruning algorithm

1.
School of Information Science and Electrical Engineering, Shan Dong Jiao Tong University, Jinan 250000, China
2.
School of Control Science and Engineering, Shandong University, Jinan 250000, China
3.
Department of Radiology, Qilu Hospital of Shandong University, Jinan 250000, China

Academic Editor: Biswajeet Pradhan

Received: 23 January 2022 Revised: 04 May 2022 Accepted: 16 May 2022 Published: 30 May 2022

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.
- image processing,
- dorsal hand vein,
- automatic injection,
- needle entry point,
- location decision
Citation: Guangyuan Zhang, Xiaonan Gao, Zhenfang Zhu, Fengyv Zhou, Dexin Yu. Determination of the location of the needle entry point based on an improved pruning algorithm[J]. Mathematical Biosciences and Engineering, 2022, 19(8): 7952-7977. doi: 10.3934/mbe.2022372

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Abstract

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.

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