Robotic manipulation of deformable objects has been a classic topic in robotics. Compared to synthetic deformable objects such as rubber balls and clothes, biological cells are highly deformable and more prone to damage. This article presents robotic manipulation of deformable cells for orientation control (both out-of-plane and in-plane), which is required in both clinical (e.g., in vitro fertilization) and biomedical (e.g., clone) applications. Compared to manual cell orientation control based on empirical experience, the robotic approach, based on modeling and path planning, effectively rotates a cell, while consistently maintaining minimal cell deformation to avoid cell damage. A force model is established to determine the minimal force applied by the micropipette to rotate a spherical or, more generally, ellipsoidal oocyte. The force information is translated into indentation through a contact mechanics model, and the manipulation path of the micropipette is formed by connecting the indentation positions on the oocyte. An optimal controller is designed to compensate for the variations of mechanical properties across oocytes. The polar body of an oocyte is detected by deep neural networks with robustness to shape and size differences. In experiments, the system achieved an accuracy of 97.6% in polar body detection and an accuracy of 0.7 $^{\circ }$ in oocyte orientation control with maximum oocyte deformation of 2.70 $\mu$ m throughout the orientation control process.

Objective: In clinical intracytoplasmic sperm injection (ICSI), a motile sperm must be immobilized before insertion into an oocyte. This paper aims to develop a robotic system for automated tracking, orientation control, and immobilization of motile sperms for clinical ICSI applications. Methods: We adapt the probabilistic data association filter by adding sperm head orientation into state variables for robustly tracking the sperm head and estimating sperm tail positions under interfering conditions. The robotic system also utilizes a motorized rotational microscopy stage and a new visual servo control strategy that predicts and compensates for sperm movements to actively adjust sperm orientation for immobilizing a sperm swimming in any direction. Results: The system robustly tracked sperm head with a tracking success rate of 96.0% and estimated sperm tail position with an accuracy of 1.08 μm under clinical conditions where the occlusion of the target sperm and interference from other sperms occur. Experimental results from robotic immo bilization of 400 sperms confirmed that the system achieved a consistent immobilization success rate of 94.5%, independent of sperm velocity or swimming direction. Conclusion: Our adapted tracking algorithm effectively distinguishes the target sperm from interfering sperms. Predicting and compensating for sperm move ments significantly reduce the positioning error during sperm orientation control. These features make the robotic system suitable for automated sperm immobilization. Significance: The robotic system eliminates stringent skill requirements in manual sperm immobilization. It is capable of manipulating sperms swimming in an arbitrary direction with a high success rate.

Measuring cell motility and morphology is important for revealing their functional characteristics. This paper presents automation techniques that enable automated, noninvasive measurement of motility and morphology parameters of single sperms. Compared to the status quo of qualitative estimation of single sperm’s motility and morphology manually, the automation techniques provide quantitative data for embryologists to select a single sperm for intracytoplasmic sperm injection (ICSI). An adapted joint probabilistic data association filter (JPDAF) was used for multi-sperm tracking and tackled challenges of identifying sperms that intersect or have small spatial distances. Since the standard differential interference contrast (DIC) imaging method has side illumination effect which causes inherent inhomogeneous image intensity and poses difficulties for accurate sperm morphology measurement, we integrated total variation norm into the quadratic cost function method, which together effectively removed inhomogeneous image intensity and retained sperm’s subcellular structures after DIC image reconstruction. In order to relocate the same sperm of interest identified under low magnification after switching to high magnification, coordinate transformation was conducted to handle the changes in the field of view caused by magnification switch. The sperm’s position after magnification switch was accurately predicted by accounting for the sperm’s swimming motion during magnification switch. Experimental results demonstrated an accuracy of 95.6% in sperm motility measurement and an error <10% in morphology measurement.