Automatic Cell Rotation Method Based on Deep Reinforcement Learning

Cell rotation is widely used to adjust cell posture in sub-cellular micromanipulations. The trajectory planning of the injection micropipette is needed, so that the cells can be rotated with the minimum deformation to reduce cell damage and keep cell viability. Due to the uncertainty of cell properties and manipulation environment, it is difficult to identify the parameters of the mechanical models in traditional robotic cell rotation methods. In this paper, deep reinforcement learning is introduced into cell manipulation for the first time to perform trajectory planning of the micropipette. We first abstract the cell rotation process by using the mechanical model and microscopic vision techniques and build a cell rotation simulation environment. Then we design a reward function by combining various factors of cell rotation and implement a reinforcement learning framework based on deep Q-learning (DQL). Finally, we train the cell rotation process based on the deep reinforcement learning algorithm. The simulation results indicate the proposed DQL agent achieved an average success rate of 97% without useless exploration. Moreover, the proposed method rotated the cells in a way that causes less mechanical damage than humans, demonstrating the DRL ability for cell rotation with high efficiency and low cell damage.

Authors

• Huiying Gong
• Yujie Zhang
• Yaowei Liu
• Qili Zhao
• Xin Zhao 0010
• Mingzhu Sun

Keywords

• Deep learning
• Training
• Uncertainty
• Q-learning
• Trajectory planning
• Deformation
• Microscopy
• Deep Reinforcement Learning
• Rotation Method
• Cell Damage
• Mechanistic Model
• Use Of Cells
• Simulation Environment
• Microscopy Techniques
• Reward Function
• Cells In Ways
• Deep Reinforcement Learning Algorithm
• Deep Q-learning
• Young’s Modulus
• Major Axis
• Training Step
• Mechanical Parameters
• Intracytoplasmic Sperm Injection
• Morphological Operations
• Deep Reinforcement Learning Agent
• Polar Body
• Angle Of Cell
• Preimplantation Genetic Diagnosis
• Reinforcement Learning Agent
• Major Axis Of The Ellipse
• Porcine Oocytes
• Robot Navigation
• Sequence Of Transitions
• Replay Buffer