Convolutional neural network transformer (CNNT) for fluorescence microscopy image denoising with improved...

Area of Science:

• Microscopy and Imaging
• Artificial Intelligence in Science
• Biomedical Engineering

Background:

• Deep neural networks (DNNs) offer potential for improving fluorescence microscopy image quality.
• Existing Convolutional Neural Network (CNN) based methods demand extensive, experiment-specific model training, limiting practical application and generalizability.
• The need for faster, more adaptable image enhancement techniques in fluorescence microscopy is critical.

Purpose of the Study:

• To introduce a novel Convolutional Neural Network Transformer (CNNT) model for enhanced fluorescence microscopy image denoising.
• To demonstrate the CNNT model's ability to achieve high-quality image reconstruction with significantly reduced training overhead.
• To validate the CNNT model's performance across diverse fluorescence microscopy techniques.

Main Methods:

• Development of a generalizable CNNT backbone model trained on pairwise high-low Signal-to-Noise Ratio (SNR) image volumes from an instant Structured Illumination Microscope.
• Implementation of a rapid adaptation strategy through fine-tuning the CNNT backbone on a small dataset (5-10 image pairs) for new experimental conditions.
• Comparative analysis against established CNN-based denoising models like 3D-RCAN and Noise2Fast.

Main Results:

• The CNNT model demonstrates superior image denoising performance compared to existing CNN-based methods.
• The proposed fine-tuning scheme drastically reduces adaptation time for new microscopes and experiments.
• The approach shows efficacy across various microscopy modalities, including wide-field, two-photon, and confocal fluorescence microscopy.

Conclusions:

• The CNNT model represents a significant advancement in deep learning for fluorescence microscopy image enhancement.
• The CNNT backbone and fine-tuning strategy offer a powerful, efficient, and versatile solution for improving image quality across different microscopy platforms.
• This work facilitates broader adoption of advanced deep learning techniques in biological imaging research.