Automatic analysis of spatio-temporal microscopy images is inevitable for state-of-the-art research in the life sciences. Recent developments in deep learning provide powerful tools for automatic analyses of such image data, but heavily depend on the amount and quality of provided training data to perform well. To this end, we developed a new method for realistic generation of synthetic 2D+t microscopy image data of fluorescently labeled cellular nuclei. The method combines spatiotemporal statistical shape models of different cell cycle stages with a conditional GAN to generate time series of cell populations and provides instance-level control of cell cycle stage and the fluorescence intensity of generated cells. We show the effect of the GAN conditioning and create a set of synthetic images that can be readily used for training and benchmarking of cell segmentation and tracking approaches.
翻译:对时空微镜图像的自动分析对于生命科学的最新研究来说是不可避免的。最近深层学习的发展为自动分析这种图像数据提供了有力的工具,但在很大程度上取决于所提供的培训数据的数量和质量,才能很好地发挥作用。为此目的,我们开发了一种新的方法,以便现实地生成带有荧光标签的荧光细胞核的合成 2D+t 显微镜图像数据。这种方法将不同细胞周期阶段的波地时统计形状模型与有条件的GAN 结合起来,生成细胞群的时间序列,并对细胞循环阶段和生成的细胞的荧光强度进行实例级控制。我们展示了GAN调节的效果,并制作了一套可以随时用于细胞分解和跟踪方法的培训和基准设定的合成图像。