Seamless Virtual Whole Slide Image Synthesis and Validation Using Perceptual Embedding Consistency.

Area of Science:

• Digital Pathology
• Computational Pathology
• Artificial Intelligence in Medicine

Background:

• Stain virtualization in digital pathology simulates stained tissue images, conserving resources.
• Generative Adversarial Networks (GANs) and unsupervised learning enable realistic image generation.
• Whole Slide Images (WSIs) pose computational challenges due to their large size, necessitating tilewise processing.

Purpose of the Study:

• To address tiling artifacts in virtual staining of WSIs caused by tilewise processing with instance normalization.
• To propose a novel perceptual embedding consistency (PEC) loss for improved virtual WSI reconstruction.
• To enhance the seamlessness and clinical interpretability of virtually stained WSIs.

Main Methods:

• Developed a novel perceptual embedding consistency (PEC) loss function for GANs.
• Implemented tilewise processing for training and inference of deep learning networks on WSIs.
• Quantitatively validated the method by comparing virtually generated images to real stained images.
• Assessed model robustness to color, contrast, and brightness perturbations.

Main Results:

• The proposed PEC loss significantly reduced tiling artifacts in reconstructed WSIs.
• Virtual WSIs exhibited more seamless reconstruction compared to state-of-the-art methods.
• The method demonstrated robustness to image perturbations, validated through a tumor segmentation task.
• Preliminary pathologist interpretation showed comparable results between real and virtual tiles.

Conclusions:

• The PEC loss is effective in generating seamless virtual WSIs by learning invariant features.
• This approach offers a promising solution for resource-efficient digital pathology workflows.
• Further validation is supported by pathologist interpretation and robustness assessments.