Polar-body detection is an essential and crucial procedure in various automatic cell manipulations. The polar body can only be observed when it is located near the focal plane of the microscope, so we need to detect the polar body during cell rotation in cell manipulations. However, three-dimensional cell rotation by micropipette causes polar-body defocus and cell/polar-body deformation, which have not been discussed in existing image-level polar-body-detection approaches. Moreover, varying sizes of the polar bodies increase the difficulty of polar-body detection. In this paper, we propose a deep-learning-based framework to realize polar-body detection in cell rotation. The detection problem is interpreted as image segmentation, which separates the polar body from the background. Then, we improve U-net, which is a typical convolutional neural network (CNN) for medical-image segmentation, so that the network can be applied to polar-body detection, especially for the detection of defocused polar bodies and polar bodies of different sizes. For CNN training, we also designed a particular image-transformation method to simulate more cell-rotation situations, including cell- and polar-body deformation, so that the deformed polar body in cell rotation would be detected by the proposed method. Experiment results show that our method achieves high detection accuracy of 98.7% on a test dataset of 1000 images, and performs well in cell-rotation processes. This method can be applied to various automatic cell manipulations in the future.
Keywords: automatic micromanipulation; cell manipulation; deep neural network; polar-body detection; somatic cell nuclear transfer.