4.6 Article

Design and Implementation of an Automatic Batch Microinjection System for Zebrafish Larvae

期刊

IEEE ROBOTICS AND AUTOMATION LETTERS
卷 7, 期 2, 页码 1848-1855

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LRA.2022.3143286

关键词

Zebrafish larvae; automatic microinjection system; machine vision; image processing; deep learning

类别

资金

  1. National Natural Science Foundation of China [52175556]
  2. Macao Science and Technology Development Fund [132/2019/A, 153/2019/A3, 022/2019/AKP]
  3. Research Committee of University of Macau [MYRG2019-00113-FST]

向作者/读者索取更多资源

This article introduces a new automated robotic system for batch microinjection of zebrafish larvae. The larvae are immobilized using a specially designed agarose supporting base, and injection targets are accurately identified using a machine vision scheme based on deep learning. Experimental results demonstrate that the robotic system provides higher injection success rate, better stability, and better performance than existing microinjection approaches.
Microinjection of zebrafish larvae is one of the most common procedures in biomedical experiments. Majority of microinjection work of zebrafish larvae in the laboratory is operated manually. Manual operation is influenced by many factors, and it is difficult to maintain steady efficiency and high success rate. To solve this problem, a novel automated robotic system is proposed for batch microinjection of zebrafish larvae in this letter. For immobilizing the zebrafish larvae in batches with standard posture, an agarose supporting base is specially designed in a Petri dish. It is fabricated to hold the larvae in different orientations, which facilitates a rapid alignment of the randomly placed larvae. To accurately identify the injection targets of zebrafish larvae, a machine vision scheme based on deep learning is established. A prototype microinjection system is developed by integrating the hardware equipment and machine vision software, which is able to complete the injection operation quickly and accurately. The experimental results reveal that the robotic system provides higher injection success rate, better stability, and better performance than those of existing microinjection approaches.

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