Robust Out-of-Plane Rotation of Biological Cells Based on Polar Body Orientation Prediction

3-D rotation of the biological cell is essential for localizing subcellular structure in various cell manipulations. However, visual detection errors of the subcellular structure affect the rotation performance in automatic cell manipulations. In this article, we enhance the robustness of out-of-plane rotation through visual detection evaluation. At first, we developed a kinematic model and simulation environment for cell rotation. We then predicted the presence and reliability of polar body detection using a posteriori probability analysis from 4 million simulations. We finally presented a robust out-of-plane rotation strategy based on polar body orientation prediction. Both simulation and experimental results with porcine oocytes demonstrated the efficiency and robustness of the proposed cell rotation method across various abnormal situations, improving the success rate of cell rotation. This work contributes a universal framework applicable to other vision-based cell rotation problems, offering a significant advancement in the field of cell manipulation.