CIEGAN: A Deep Learning Tool for Cell Image Enhancement - Sun, Qiushi; Yang, Xiaochun; Guo, Jingtao; Zhao, Yang; Liu, Yi (Frontiers in Genetics, 2022)
Long-term live-cell imaging technology has emerged in the study of cell culture and development, it is expected to elucidate the differentiation or reprogramming morphology of cells and the dynamic process of interaction between cells. There are some advantages of this technique: non-invasive, high-throughput, and low-cost, it can help researchers explore phenomena that are difficult to observe previously. Although many challenges arise in the real-time process, for example, low-quality micrographs are often being obtained due to unavoidable human factors or technical factors in the long-term experimental period. Moreover, some core dynamics in the developmental process are rare and fleeting in imaging observation, and difficult to be recaptured again. Therefore, this paper proposes a deep learning method for microscope cell images enhancement to reconstruct the sharp images. We combine generative adversarial nets and various loss functions to make the blurry images sharp again, which is much more convenient for researchers to carry out further analysis. This technology can not only make up the blurry image of critical moments of the development process through image enhancement but also allows long-term live-cell imaging to find a balance between imaging speed and image quality. Furthermore, the scalability of this technology makes the methods perform also well in fluorescence image enhancement. Finally, the method is tested in the long-term live-cell imaging of human-induced pluripotent stem cell-derived cardiomyocytes differentiation experiment, it can greatly improve the image space resolution ratio.