Adaptive Color Separation for Multi-Stain Brightfield Imaging
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Solution Overview
Problem
Existing methods for color separation in medical imaging, such as spectral unmixing, struggle with variations in tissue type, age, and staining processes, leading to incorrect separation and physiologically implausible results, especially when dealing with multiple stains in brightfield images.
Innovation Solution
The system adaptively optimizes reference vectors based on specific assay information, applying iterative non-constrained color deconvolution and correlation with rules to ensure physiologically plausible results, allowing for the unmixing of images with multiple stains beyond the typical three-color channels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standard reference spectra are used for color separation, then the unmixing process is simple and fast, but the results contain artifacts and are physiologically implausible due to variations in tissue type, age, and staining processes
Solution Approach 1:
The system performs preliminary action by acquiring reference spectra from actual assay images before unmixing. Instead of using pre-defined standard spectra, the system first captures images of the actual tissue sample with stains applied, extracts reference spectra from these real samples, and then uses these customized references for color separation. This preliminary adaptation to the specific tissue and staining conditions eliminates artifacts and ensures physiologically plausible results.
Solution Approach 2:
The system changes the parameters of reference spectra by adjusting them to match the specific assay conditions. The reference spectra are not fixed but are modified based on the actual tissue type, age, and staining process variations. This parameter adaptation allows the unmixing algorithm to account for real-world variations and produce accurate stain concentration measurements.
2Adaptability or versatility
If conventional color deconvolution is applied to brightfield images with more than 3 stains, then the mathematical solution becomes ambiguous or impossible, but the system needs to analyze complex biological specimens with multiple stains
Solution Approach 1:
The system transitions from the traditional 3-color channel space to a higher-dimensional spectral space by utilizing full spectral information across multiple wavelengths. Instead of being constrained by the 3x3 matrix limitation of conventional color deconvolution, the system uses spectral signatures that provide additional dimensional information, enabling reliable unmixing of more than 3 stains through increased informational dimensions.
Solution Approach 2:
The system introduces spectral unmixing as an intermediary approach between raw image data and final stain concentration measurements. This intermediary process uses reference spectra extracted from actual assay images to create a mapping between the observed color channels and the underlying stain concentrations, providing a reliable solution even when the number of stains exceeds the number of color channels.
3Measurement precision
If iterative optimization of reference vectors is performed, then the unmixing results are physiologically plausible and accurate, but the processing time and computational resources increase
Solution Approach 1:
The system implements self-service by automatically extracting reference spectra from the assay images themselves without requiring manual input or external calibration standards. The algorithm autonomously identifies and extracts spectral signatures from the actual tissue sample, performs iterative optimization to refine these references, and applies them to the unmixing process. This self-contained approach achieves high accuracy while minimizing manual intervention time.
Data Source
AI summary
The subject disclosure presents systems and methods for separating colors in an image by automatically and adaptively adjusting reference vectors based on information specific to the assay being imaged, resulting in an optimized unmixing process that provides stain information that is physically and physiologically plausible. The reference vectors are optimized iteratively, based on minimizing non-negative color contributions, background contributions, high-frequencies in color channels specific to background or unwanted fluorescence, signals from known immunohistochemical markers, and pairs of stains known to carry physiologically independent information. Adjustments to the reference vectors may be allowed within a range that is predetermined based on measuring colors from multiple input images.