Robotic Cardinal Vein Microinjection of Zebrafish Larvae Based on 3D Positioning

Sun, Mingzhu; Lu, Li; Yao, Yatong; Wang, Yiwen; Gong, Huiying; Gao, Qian; Chen, Dongyan; Zhao, Xin

doi:10.1109/icra48506.2021.9561991

Cited by 8 publications

(2 citation statements)

References 18 publications

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“…This study aims to achieve automated in vivo transparent vessel segmentation and identification for further subsequent research, for example, cardiovascular research (Li et al, 2019) and in vivo biological micromanipulations (Chan & Liebling, 2015;Menon et al, 2016;Sun et al, 2021). Our validations show that the proposed method identifies the vascular structure with high connectivity and integrity when applied to zebrafish embryos.…”

Section: Discussionmentioning

confidence: 90%

“…In vivo transparent vessels can be observed directly using a light microscope, so researchers can observe the whole process of angiogenesis and evaluate the arteriolar-to-venular ratio (Daien et al, 2013;Seidelmann et al, 2016) to study vascular architecture, cardiovascular development, disease and the effect of drugs without complex preprocessing. Moreover, transparent vessels are used in some biological micromanipulations, such as in vivo cardinal vein microinjection (Sun et al, 2021), in vivo cell tracking (Menon et al, 2016), and blood flow velocity measurement (Chan & Liebling, 2015), since they provide a clear microscopic view to achieve real-time visual feedback.…”

Section: Introductionmentioning

confidence: 99%

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A Machine Learning Method for Automated In Vivo Transparent Vessel Segmentation and Identification Based on Blood Flow Characteristics

Sun

Wang

et al. 2022

Microsc Microanal

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In vivo transparent vessel segmentation is important to life science research. However, this task remains very challenging because of the fuzzy edges and the barely noticeable tubular characteristics of vessels under a light microscope. In this paper, we present a new machine learning method based on blood flow characteristics to segment the global vascular structure in vivo. Specifically, the videos of blood flow in transparent vessels are used as input. We use the machine learning classifier to classify the vessel pixels through the motion features extracted from moving red blood cells and achieve vessel segmentation based on a region-growing algorithm. Moreover, we utilize the moving characteristics of blood flow to distinguish between the types of vessels, including arteries, veins, and capillaries. In the experiments, we evaluate the performance of our method on videos of zebrafish embryos. The experimental results indicate the high accuracy of vessel segmentation, with an average accuracy of 97.98%, which is much more superior than other segmentation or motion-detection algorithms. Our method has good robustness when applied to input videos with various time resolutions, with a minimum of 3.125 fps.

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Section: Discussionmentioning

confidence: 90%

Section: Introductionmentioning

confidence: 99%

A Machine Learning Method for Automated In Vivo Transparent Vessel Segmentation and Identification Based on Blood Flow Characteristics

Sun

Wang

et al. 2022

Microsc Microanal

Self Cite

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Design of an automated robotic microinjection system for batch injection of zebrafish embryos and larvae

Guo,

Ai,

et al. 2024

Microsyst Nanoeng

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This paper presents the design of a vision-based automated robotic microinjection system for batch injection of both zebrafish embryos and larvae. A novel visual recognition algorithm based on an automatic threshold and excessive dilatation is introduced to accurately identify the center of zebrafish embryos and larval yolks. A corresponding software system is developed using the producer-consumer model as the framework structure, and a friendly user interface is designed to allow operators to choose from a range of desired functions according to their different needs. In addition, a novel microstructural agarose device is designed and fabricated to simultaneously immobilize mixed batches of embryos and larvae. Moreover, a prototype microinjection system is fabricated by integrating hardware devices with visual algorithms. An experimental study is conducted to verify the performance of the robotic microinjection system. The results show that the reported system can accurately identify zebrafish embryos and larvae and efficiently complete batch microinjection tasks of the mixtures with an injection success rate of 92.05% in 13.88 s per sample. Compared with manual and existing microinjection systems, the proposed system demonstrates the merits of versatility, excellent efficiency, high success rate, high survival rate, and sufficient stability.

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Height Localization of Micropipette in Zebrafish Embryo Penetration Based on Force Sensor

Liu

et al. 2022

2022 7th Asia-Pacific Conference on Intelligent Robot Systems (ACIRS)

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Robotic Cardinal Vein Microinjection of Zebrafish Larvae Based on 3D Positioning

Cited by 8 publications

References 18 publications

A Machine Learning Method for Automated In Vivo Transparent Vessel Segmentation and Identification Based on Blood Flow Characteristics

A Machine Learning Method for Automated In Vivo Transparent Vessel Segmentation and Identification Based on Blood Flow Characteristics

Design of an automated robotic microinjection system for batch injection of zebrafish embryos and larvae

Height Localization of Micropipette in Zebrafish Embryo Penetration Based on Force Sensor

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