Generative Images of Microscopy Cell Arrangements Using AI Conditional Training

Advances in artificial intelligence-driven techniques are poised to revolutionize our understanding of cellular biology. Synthetic imaging capabilities elevate the precision and efficiency of analytical methods in cellular microscopy. These advancements have largely benefited cell segmentation, dynamics, and interaction analysis. In contrast, only a limited number of contributions have considered conditional generation. We present an innovative image-to-image translation scheme to generate high-quality cell microscopy images from controlled patterns. Our method accommodates both manual and automated annotated data. It uses a U-Net structure trained on patterns generated from actual cell microscopy images. We evaluated the efficacy of this approach using two distinct datasets, demonstrating its capacity to generate large-scale, highfidelity cellular images. The proposed methodology has significant potential for data augmentation in machine learning applications and elucidating complex cellular dynamics, including migration patterns and proliferation rates. Our results open the door to high-resolution dynamic analysis of cellular processes.