μPIX: Leveraging Generative AI for Enhanced, Personalized and Sustainable Microscopy

Abstract

Fluorescence microscopy is a critical tool in bio-cellular research, enabling the visualization of biological tissues and cellular structures. However, the inevitable aging of microscopes can degrade their performance posing challenges for long-term scientific investigations. In this study, we introduce {micro}PIX, a personalized deep learning workflow based on a Generative Adversarial Network (GAN) utilizing a Pix2Pix architecture. The network is trained in a supervised manner to denoise images, optimize pre-processing for binary segmentation, and compensate for equipment aging. Our results, evaluated using standard image quality and binary segmentation metrics, demonstrate that {micro}PIX outperforms popular deep learning architectures based on convolutional auto-encoder networks for similar tasks. Additionally, our generative model effectively rejuvenates older detectors to perform on par with newer ones, not only by improving image quality but also by preserving resolution in depth and maintaining a near-linear response between original and generated images in terms of pixel intensity (crucial for quantitative imaging). These findings suggest that generative deep learning approaches can significantly contribute to more sustainable, cost-effective microscopy, fostering continued innovation and discovery in biological research.