Digital In-Line Hologram Microscopy for Virtual Tissue Staining
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Solution Overview
Problem
The staining process in digital pathology imaging, particularly using histochemical dyes like Hematoxylin and Eosin, introduces variability and is time-consuming, costly, and can deform specimen structures, complicating automated image analysis.
Innovation Solution
A method using a digital in-line hologram microscope scanner that captures phase-shifted holograms at different wavelengths to create a quantitative dispersion image, simulating chemical staining through virtual staining, reducing variability and costs by avoiding chemical dyes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If chemical staining is used to provide imaging contrast, then sufficient imaging contrast is achieved, but the specimen shows huge variability in appearance and structures are deformed
Solution Approach 1:
The patent replaces chemical staining processes with optical measurement techniques. Specifically, it uses quantitative phase imaging to measure optical path length differences caused by specimen structures, and uses machine learning models trained on stained images to predict stain appearance from phase images, thereby eliminating chemical processes while maintaining imaging contrast and consistency
Solution Approach 2:
The patent creates a digital copy or representation of the staining effect through machine learning. The model learns the mapping between phase images and stained images, then generates synthetic stained images from new phase images, effectively copying the appearance of chemically stained specimens without using chemicals
2Illumination intensity
If chemical staining is applied to tissue specimens, then imaging contrast is improved, but processing time increases and costs increase
Solution Approach 1:
The patent replaces time-consuming chemical staining processes with rapid optical phase measurement and computational image generation. The phase imaging captures specimen information without chemicals, and the machine learning model instantly generates contrast-enhanced images, eliminating hours of staining processing while maintaining diagnostic quality
3Illumination intensity
If chemical staining is used to achieve imaging contrast, then sufficient contrast is obtained, but the process becomes expensive especially with non-common reagents
Solution Approach 1:
The patent replaces expensive chemical reagents with optical measurement and computational methods. The phase imaging system uses standard optical components to measure specimen properties, and machine learning algorithms generate contrast images without requiring any chemical stains, eliminating reagent costs entirely
Solution Approach 2:
The patent creates synthetic stained images that replicate the appearance of chemically stained specimens. By training machine learning models on stained image data, the system can generate equivalent visual information from unstained phase images, copying the diagnostic value of expensive staining processes at minimal computational cost
4Adaptability or versatility
If multiple staining protocols are used across different labs, then various imaging requirements are met, but variability in dye appearance increases making automated analysis difficult
Solution Approach 1:
The patent changes the measurement parameter from chemical staining to optical phase measurement. Phase imaging measures the optical path length through specimen structures, providing a quantitative, protocol-independent measurement that is invariant to chemical variations. The machine learning model then transforms these consistent phase measurements into standardized contrast images suitable for automated analysis
Solution Approach 2:
The patent creates a universal imaging approach that works across different laboratories and specimen types without requiring lab-specific staining protocols. The phase imaging system captures fundamental optical properties of specimens, and the machine learning model generalizes across different tissue types and imaging conditions, providing consistent results universally
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances image analysis consistency, reduces time and costs, and maintains specimen structure integrity by creating virtually stained images without chemical dyes.
Implementation Method 1
acquiring with at least three different illumination light wavelengths respectively one or more phase-shifted holograms of an object at an image sensor
Data Source
AI summary
A method comprising determining a quantitative dispersion image of an object based on a set of quantitative phase images, each quantitative phase image of the set of quantitative phase images having been obtained with a respective different illumination light wavelength.