Multi-Modal Stain Learning Engine for Virtual Tissue Imaging
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Solution Overview
Problem
Physical staining methods for tissue samples are time-consuming, costly, and contribute to workplace and environmental pollution, limiting their efficiency in medical research and diagnosis.
Innovation Solution
A system and method utilizing a multi-modal stain learning engine, specifically a generative adversarial network, to generate virtually stained images of tissue samples, allowing for the simulation of different stains without physical staining, which includes obtaining images of key and adjacent sample sections, aligning and processing them to create accurate stained images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If physical staining methods are used to stain tissue sections, then certain cells, features or structures become more visible under the microscope, but the process becomes time-consuming and costly
Solution Approach 1:
The patent uses a generative adversarial network to create a virtual copy of the stained tissue image from an unstained image. Instead of physically staining the tissue section, the AI model learns the staining transformation from training data and applies it computationally, producing an image that visually replicates what the stained tissue would look like, thereby eliminating the time-consuming physical staining process while maintaining diagnostic visibility
Solution Approach 2:
The patent replaces the mechanical/chemical staining process with an computational/AI-based process. The generative adversarial network substitutes the physical chemical reactions of staining agents with digital image processing and neural network transformations, achieving the same visual enhancement effect without the physical staining steps
2Measurement precision
If physical staining methods are used to stain tissue sections, then contrast is improved for microscopy, but the cost increases
Solution Approach 1:
The system creates a digital copy of the stained appearance through AI processing rather than consuming physical staining reagents. The generative model produces a synthetic stained image that replicates the contrast and visual features of physical staining without requiring expensive staining agents, reducing material costs while maintaining image quality
Solution Approach 2:
The patent substitutes expensive chemical staining processes with computational algorithms. The generative adversarial network performs the contrast enhancement function that would otherwise require costly staining reagents, replacing material-intensive processes with computation-intensive processing that has lower marginal costs
3Measurement precision
If physical staining methods are used, then tissue sections can be visualized under microscope, but workplace and environmental pollution increases
Solution Approach 1:
The system generates a virtual stained image that copies the visual appearance of physically stained tissue without requiring physical staining agents. This digital copying approach eliminates the release of chemical pollutants into the workplace environment and surrounding ecosystem, as no physical staining reagents are applied or disposed of
Solution Approach 2:
The patent replaces chemical staining processes that generate pollution with a computational imaging process. The generative adversarial network achieves tissue visualization through digital transformation rather than chemical reaction, eliminating the harmful emissions, chemical waste, and environmental contamination associated with physical staining methods
Data Source
AI summary
A system and method for generating a stained image including the steps of obtaining a first image of a key sample section; and processing the first image with a multi-modal stain learning engine arranged to generate at least one stained image, wherein the at least one stained image represents the key sample section stained with at least one stain.