The application of convolution neural network based cell segmentation during cryopreservation.

For most of the cells, water permeability and plasma membrane properties play a vital role in the optimal protocol for successful cryopreservation. Measuring the water permeability of cells during subzero temperature is essential. So far, there is no perfect segmentation technique to be used for the image processing task on subzero temperature accurately. The ice formation and variable background during freezing posed a significant challenge for most of the conventional segmentation algorithms. Thus, a robust and accurate segmentation approach that can accurately extract cells from extracellular ice that surrounding the cell boundary is needed. Therefore, we propose a convolutional neural network (CNN) architecture similar to U-Net but differs from those conventionally used in computer vision to extract all the cell boundaries as they shrank in the engulfing ice. The images used was obtained from the cryo-stage microscope, and the data was validated using the Hausdorff distance, means ±â€¯standard deviation for different methods of segmentation result using the CNN model. The experimental results prove that the typical CNN model extracts cell borders contour from the background in its subzero state more coherent and effective as compared to other traditional segmentation approaches.

A microfluidic platform with cell-scale precise temperature control for simultaneous investigation of the osmotic responses of multiple oocytes.

The temperature-dependent oocyte membrane permeability plays a significant role in oocyte cryopreservation, such as optimizing the addition/removal of cryoprotective agents and the rate of cooling/rewarming. However, the systems for studying the temperature dependence of oocyte membrane permeability are either too complicated or unable to achieve wide-range precise temperature control. In addition, these systems cannot achieve the simultaneous observation of multiple oocytes. Here, we report a novel microfluidic platform that combines a precise local temperature heater/detector and a simple global water bath to achieve wide-range accurate temperature control without increasing the difficulty of fabrication, and it also realizes non-interfering, position-controllable and non-missing capture of multiple oocytes for parallel experiments to increase throughput. The permeability coefficients (Lp, Ps) of the mouse oocyte membrane exposed to cryoprotective agents (1.5 M EG and 1.5 M PG) at four temperatures (4, 15, 25 and 37 °C) are consistent with those reported in previous works, which proves the feasibility and practicality of the microfluidic platform in this study.