μPIX: Leveraging Generative AI for Enhanced, Personalized and Sustainable Microscopy
Bon, G.; Sapede, D.; Matthews, C.; Daian, F.
Show 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.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- A Deep Learning approach for time-consistent cell cycle phase prediction from microscopy data 95%
- DeepMIB: User-friendly and open-source software for training of deep learning network for biological image segmentation 95%
- RETINA: Reconstruction-based Pre-Trained Enhanced TransUNet for Electron Microscopy Segmentation on the CEM500K Dataset 95%
Similar papers in this journal
- Deep Learning Classification of Lipid Droplets in Quantitative Phase Images 96%
- Small hand-designed convolutional neural networks outperform transfer learning in automated cell shape detection in confluent tissues 96%
- SAMCell: Generalized Label-Free Biological Cell Segmentation with Segment Anything 95%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.