CNN-based visual processing approach for biological sample microinjection systems

Abstract

In vitro microinjection is one of the main approaches to deliver the specimens to the target. However, conventional microinjection is mostly operated by very experienced technicians and the successful rate is relatively low. Moreover, the manual injection process is also very time consuming and tiring because extra attention needs to be paid for precise operation. As a result, the workers suffer from fatigue and the efficiency remains low. To improve the efficiency, researchers have developed automated microinjection systems to free the technicians from this laborious work. Zebrafish has been more popular than ever for the last decade due to its genetic advantages. It is claimed that the gene of Zebrafish highly resembles to human, which results in the fact that more biological and medical practice has been carried out with Zebrafish larvae or embryos. This paper proposes an innovation convolutional neural network based visual processing strategy for biological sample manipulation and microinjection systems. The proposed approach allows high throughput injection with considerable injecting precision. The strategy is to firstly locate a number of biological samples in the visual field, then the proposed visual algorithm identifies the number of the samples and detects the orientation of each sample. After which the samples are rotated one after another by a rotation plate to the desired angle for injection. An optimized rotation strategy is performed to achieve minimum total rotation angle. A path free of collision is also generated by A-star algorithm for the holding pipette. The experiment results show significant improvement in orientation efficiency and the accuracy of multi-sample rotation is enhanced to some extent.