Robotic Cell Manipulation for Blastocyst Biopsy

Abstract

Soft tissue cutting is used for incision, separation and removal of tissues or cells. Due to high deformation of soft tissues resulting from their viscosity and elasticity, it is challenging to accurately cut the tissue along a desired path and control the force applied to the tissue for reducing invasiveness, especially at the microscale. This paper presents a robotic biopsy system for cutting and collecting trophectoderm cells from a highly deformable blastocyst. The system, for the first time, enables TE cell junction detection for laser ablation throughout the blastocyst biopsy process by using a convolutional neural network. The overall detection error was 2.13% in every 1,000 cell junctions with position RMSE of $1.63\ \mu \mathrm{m}\pm 0.29\ \mu \mathrm{m}$. A dynamics model was developed to describe the motion of the trophectoderm cells inside a biopsy micropipette. Based on this model, an adaptive control method was developed for trophectoderm cell aspiration and positioning inside the biopsy micropipette. Experimental results revealed that the controller was capable of effectively compensating for the cell positioning error by updating the varying system parameters according to the adaptation law. The success rate was 100%, the cell aggregate positioning accuracy was $\pm 1\ \mu \mathrm{m}$, the average settling time was 2 s, and the largest overshoot was $4.3\ \mu \mathrm{m}$. Compared to manual blastocyst biopsy, the robotic biopsy system shortened the blastocyst's recovery time (35 min vs. 50 min) which indicates lower invasiveness.