![]() 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. 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. The detection problem is interpreted as image segmentation, which separates the polar body from the background. In this paper, we propose a deep-learning-based framework to realize polar-body detection in cell rotation. Moreover, varying sizes of the polar bodies increase the difficulty of polar-body detection. 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. 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. Polar-body detection is an essential and crucial procedure in various automatic cell manipulations.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |