Random Multi-Channel Image Synthesis for Multiplexed Immunofluorescence Imaging.

Area of Science:

• Biomedical Imaging
• Computational Pathology
• Artificial Intelligence in Medicine

Background:

• Multiplex immunofluorescence (MxIF) enables high-sensitivity, single-cell mapping using extensive antibody staining on tissue sections.
• MxIF is limited by tissue depletion from iterative staining and potential "missing stain" data due to imaging failures.
• Restoring missing stain information digitally is crucial for maximizing data from scarce tissue samples.

Purpose of the Study:

• To develop and validate a novel image synthesis approach for restoring missing stain data in MxIF imaging.
• To address the challenge of "missing stain" in MxIF without further physical tissue loss.
• To create a comprehensive framework for synthesizing missing molecular marker data in MxIF.

Main Methods:

• Proposed pixN2N-HD, a multi-channel, high-resolution generative adversarial network (GAN) for image synthesis.
• Developed an "N-to-N" strategy to efficiently handle multiple missing stain scenarios.
• Conducted a comprehensive experimental study on synthesizing eleven MxIF structural molecular markers (epithelial and stromal).

Main Results:

• The pixN2N-HD framework successfully synthesized missing stain images for MxIF data.
• The "N-to-N" strategy drastically reduced computational time from an estimated four years to 20 hours for all missing stain combinations.
• This work represents the first extensive experimental investigation into cross-stain synthesis for MxIF.

Conclusions:

• Deep image synthesis offers a promising solution to mitigate "missing stain" issues in MxIF imaging.
• pixN2N-HD provides an efficient and effective method for digital restoration of MxIF data, preserving tissue and reducing computational burden.
• This approach advances MxIF capabilities, enabling more comprehensive biomarker analysis from limited tissue samples.