Quantifying Biomarker Signals in Stain Aggregates
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Solution Overview
Problem
Current technologies face limitations in performing quantitative analysis of immunohistochemical and in situ hybridization signals, particularly in accurately estimating the amount of biomarkers in signal aggregate blobs, which hinders efficient clinical interpretation and quick analysis of biological samples.
Innovation Solution
An image-analysis system and method that detects and quantifies protein or nucleic acid signals by calculating the total number of spots in sub-regions, using descriptive signal features like intensity, blurriness, and roundness, and estimating the number of predictive spots in signal aggregates through histogram analysis and segmentation techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated techniques are used to estimate signal amounts in signal aggregate blobs, then productivity is improved, but measurement precision deteriorates due to the complexity of quantifying aggregated signals
Solution Approach 1:
The patent segments signal aggregate blobs into individual spot signals through image processing. The system divides the complex task of quantifying aggregated signals into manageable steps: detecting individual spots within aggregates, separating their signals computationally, and summing them to obtain total signal amounts. This segmentation enables automated processing while maintaining measurement precision by treating each spot as a distinct quantifiable unit.
Solution Approach 2:
The patent introduces an intermediary computational model that bridges the gap between raw aggregate signal images and accurate quantification. The system uses trained machine learning models or reference databases as intermediaries to translate complex aggregate patterns into reliable spot count estimates. This intermediary layer enables automated analysis to achieve precision comparable to manual expert analysis.
2Measurement precision
If manual analysis methods are used for signal quantification, then measurement precision is maintained, but productivity deteriorates due to time-consuming analysis
Solution Approach 1:
The patent replaces manual mechanical analysis with automated image processing and computational algorithms. The system uses computer vision techniques to detect, segment, and quantify spot signals automatically, substituting the manual visual inspection process with algorithmic analysis. This substitution maintains measurement precision through consistent application of detection criteria while dramatically increasing productivity by processing images without human time constraints.
Solution Approach 2:
The patent enables the analysis system to perform self-service through automated detection and quantification algorithms that operate independently without human intervention. The system automatically identifies signal aggregates, segments individual spots, quantifies their signals, and generates results autonomously. This self-service capability eliminates the time-consuming manual analysis step while maintaining consistent measurement precision through standardized computational procedures.
3Measurement precision
If complex image processing techniques are applied to separate spot signals in aggregates, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models or establishing reference databases before actual signal quantification. The system performs preliminary learning from training datasets that contain examples of signal aggregates with known spot compositions. This preliminary action enables the system to automatically recognize and separate spot signals in new images using the pre-acquired knowledge, reducing the complexity of real-time processing while maintaining high measurement precision.
Solution Approach 2:
The patent utilizes parameter changes by adjusting image processing parameters such as threshold values, segmentation criteria, and detection sensitivity based on training results or reference data. The system dynamically modifies these parameters to optimize the separation of spot signals within aggregates. By changing parameters rather than complexifying the overall system architecture, the patent achieves high measurement precision with manageable device complexity.
Data Source
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AI summary
The present disclosure provides for systems and methods for detecting and estimating signals corresponding to one or more bio markers in biological samples stained for the presence of protein and/or nucleic acid biomarkers.