Virtual Staining Machine Learning Logic for Tissue Analysis
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Solution Overview
Problem
The interpretation of tissue sample images stained with chemical stains can be limited by the specific staining laboratory process used, making it difficult for pathologists to provide second opinions without re-staining the sample, which is time-consuming and costly, and introduces potential variations in diagnosis.
Innovation Solution
A method of virtual staining using machine-learning logic to generate multiple output images of a tissue sample with different colorings associated with various staining laboratory processes, allowing for customized virtual stains that can mimic the appearance of different chemical stains without the need for physical re-staining.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If physical re-staining is performed to provide second opinions, then different staining laboratory processes can be applied, but time consumption and cost increase significantly
Solution Approach 1:
The patent uses a machine learning model to generate a virtual staining result that copies the appearance of a chemical stain applied by a second staining laboratory process. Instead of physically re-staining the tissue sample, the system creates a digital copy of what the stain would look like, thereby providing the adaptability to apply different staining processes while eliminating the time-consuming physical re-staining step
Solution Approach 2:
The patent replaces the mechanical/chemical process of physical re-staining with a computational machine learning approach. The machine learning model processes the original staining result and transforms it into a virtual staining result that mimics the appearance of applying a different staining laboratory process, thereby substituting the physical re-staining operation with an information processing operation
2Adaptability or versatility
If physical re-staining is performed to provide second opinions, then different staining laboratory processes can be applied, but costs increase due to additional materials and processing
Solution Approach 1:
The system creates a digital copy of the staining appearance through machine learning rather than physically applying new stains. This copying approach allows the adaptability to explore different staining laboratory processes while eliminating the material costs and energy consumption associated with actual physical re-staining operations
Solution Approach 2:
The patent substitutes the energy-intensive physical re-staining process with a computational approach. The machine learning model performs the transformation digitally, replacing the chemical materials and processing energy required for physical re-staining with information processing operations that consume significantly less energy and resources
3Measurement precision
If physical re-staining is performed, then accurate diagnosis can be obtained, but potential variations in diagnosis are introduced due to different staining processes
Solution Approach 1:
The system creates a virtual copy of what the tissue sample would look like under a different staining process, maintaining the underlying tissue structure and features consistent across different virtual staining representations. This allows accurate diagnosis through different staining perspectives while preserving the stability of the actual tissue sample appearance
Solution Approach 2:
The patent replaces physical re-staining with a computational transformation that maintains consistency of the tissue sample's underlying structure. The machine learning model transforms the appearance to match different staining laboratory processes while preserving the stable, invariant features of the actual tissue sample, thereby enabling accurate diagnosis without introducing variations from physical re-staining
Data Source
AI summary
A method of virtual staining of a tissue sample includes obtaining imaging data depicting the tissue sample. The method also includes processing the imaging data in at least one machine-learning logic, the at least one machine-learning logic being configured to provide multiple output images all comprising a given virtual stain of the tissue sample, the multiple output images depicting the tissue sample comprising the given virtual stain at different colorings associated with different staining laboratory processes. The method further includes obtaining, from the at least one machine-learning logic, at least one output image of the multiple output images.