Multi-spectral Imaging with Common Stain for Tissue Component Classification
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Solution Overview
Problem
Chromogenic staining techniques face challenges in distinguishing and classifying multiple stains in tissue samples due to overlapping spectral characteristics, making it difficult to accurately determine the presence and distribution of different stains and structures, especially when they have similar absorption and emission properties.
Innovation Solution
A method involving spectral imaging and processing, where a sample with multiple components is labeled with fewer stains, including a common stain, and analyzed using an electronic processor to classify and distinguish components based on signal strength, shape, spectral features, and textural features, with optional histological stains to enhance differentiation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple stains with similar spectral characteristics are used to label different components, then the ability to label multiple components is improved, but the difficulty of distinguishing and detecting each stain is worsened
Solution Approach 1:
The patent segments the spectral detection process into multiple wavelength channels, allowing differentiation of stains with similar spectral characteristics by analyzing their spectral signatures at different wavelengths. This enables accurate detection and quantification of multiple stains even when they have overlapping spectral properties.
Solution Approach 2:
The patent changes the detection parameter from single-wavelength intensity measurement to multi-wavelength spectral signature analysis. By measuring absorbance or emission at multiple wavelengths and comparing the spectral patterns, the system can distinguish between different stains that appear similar at any single wavelength.
2Device complexity
If fewer stains are used to label multiple components, then the complexity of the staining protocol is reduced, but the ability to accurately classify components is worsened
Solution Approach 1:
The patent employs a universal spectral imaging approach that can detect and differentiate multiple stain types using a single imaging system. The system uses spectral unmixing algorithms to deconvolute overlapping spectral signals, allowing accurate classification of components labeled with fewer, more versatile stains.
Solution Approach 2:
The patent introduces spectral unmixing algorithms as an intermediary between the staining process and component identification. These algorithms process the spectral image data to separate contributions from different stains, enabling accurate classification even when multiple components share common stains.
3Measurement precision
If spectral imaging is used to distinguish stains with similar absorption properties, then the precision of stain identification is improved, but the complexity of image processing is worsened
Solution Approach 1:
The patent performs preliminary spectral unmixing to separate the spectral contributions of different stains before final classification. By pre-processing the spectral images to isolate individual stain signals, the system simplifies subsequent analysis and improves identification precision while managing processing complexity through structured algorithmic approaches.
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 allows for accurate classification and quantification of multiple components in tissue samples by decomposing spectral images into unmixed sets, enabling precise identification and relative amount determination of stained components, even when they share common spectral distributions.
Implementation Method 1
stains that have similar spectral absorption and/or emission characteristics
Implementation Method 2
stains that emit or absorb with a common spectral distribution
Data Source
AI summary
A method including: providing a sample with M components to be labeled, where M>2; labeling the components with N stains, where N<M so that at least two components are labeled with a common stain; obtaining a set of spectral images of the sample; classifying different parts of the sample into respective classes that distinguish the commonly stained components based on the set of spectral images; and determining relative amounts of multiple ones of the M components in different regions of the sample. Related apparatus are also disclosed.