DeepHCS⁺⁺: Bright-field to fluorescence microscopy image conversion using multi-task learning with adversarial losses...

Area of Science:

• Biomedical Imaging
• Computational Biology
• Cell Biology

Background:

• High-content drug screening relies on fluorescence microscopy to analyze cellular biomarkers like apoptosis, nuclei, and cytoplasm.
• Traditional fluorescence imaging requires extensive tissue preparation, limiting throughput and increasing labor.
• Automating fluorescence image generation from bright-field microscopy can significantly streamline drug discovery processes.

Purpose of the Study:

• To develop a novel microscopy image translation method for converting bright-field images into multiple fluorescence channels.
• To enable visualization of cell apoptosis, nuclei, and cytoplasm from a single bright-field image.
• To enhance the efficiency and throughput of high-content drug screening.

Main Methods:

• A deep convolutional neural network (CNN) was trained using paired bright-field and fluorescence microscopy images.
• The model employed multi-task learning with adversarial losses for accurate and realistic image generation.
• The method was trained on end-to-end image-to-image translation using a dataset of glioblastoma patient samples.

Main Results:

• The proposed method successfully generated synthetic fluorescence images comparable in accuracy to real fluorescence microscopy images.
• Quantitative metrics including cell number correlation (CNC), PSNR, SSIM, and R² correlation validated the model's efficacy.
• The generated images accurately represented cell apoptosis, nuclei, and cytoplasm.

Conclusions:

• The novel deep learning-based image translation method automates the generation of crucial fluorescence biomarkers from bright-field images.
• This approach significantly reduces sample preparation time and labor, thereby increasing screening throughput.
• The method provides a powerful tool for analyzing drug responses in high-content screening applications.