Posture adjustment and robust microinjection of zebrafish larval heart

Unlike cells or embryos, zebrafish have a complex physiological structure, which poses challenges to posture recognition and adjustment during microinjection. Furthermore, zebrafish surface pigments exhibit strong interference with visual servo-based injection control, thus, affecting the success of microinjection and the subsequent survival rate. To address these challenges, we developed an automated microinjection system for the zebrafish heart that has advantages of high accuracy and success rate and avoids biological sample contamination. A convolutional neural networks (CNN) deep learning model is employed to determine the body axis posture. To solve the problems of blocked needle and abnormal tip positioning induced by zebrafish surface pigment during the injection process, an adaptive robust Kalman filter is proposed to suppress the abnormal values of visual feedback. Experimental results show that the success rate of body axis recognition based on the employed deep learning model exceeds 95%, and the proposed adaptive Kalman filter effectively suppresses the visual outliers, satisfying the requirements of high-precision injection for the zebrafish heart.