Computational Pathway Correlation Analysis for Tissue Imaging
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Solution Overview
Problem
Current diagnostic systems lack the capability to effectively measure and visualize correlations between the densities of cytotoxic T-cells and cancer cells, which is crucial for understanding immune interactions and evaluating the efficacy of cancer treatment drugs.
Innovation Solution
A pathway protein correlation value determining and visualization system that uses high-resolution digital imaging of tissue samples, dual staining with antibodies specific to cytotoxic T-cells and cancer cells, and computational analysis to calculate and display correlation coefficients, allowing for the identification of correlations and their visualization within tissue samples.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If computational analysis is performed to calculate correlation coefficients between cell densities, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent introduces a computational analysis system as an intermediary between the stained tissue sample and the correlation measurement. This system includes software modules that automatically perform image processing, cell segmentation, density calculation, and correlation coefficient determination. The intermediary computational layer handles the complex analysis tasks, enabling precise correlation measurements while managing system complexity through modular software design.
Solution Approach 2:
The patent replaces manual or mechanical methods of cell counting and correlation analysis with computational and automated image processing techniques. Digital imaging systems capture the stained tissue, and algorithms automatically identify cells, calculate densities, and determine correlation coefficients, substituting complex manual analytical processes with automated computational methods that improve precision while streamlining the overall system.
2Measurement precision
If spatial analysis of cell densities is performed, then measurement precision is improved, but ease of operation deteriorates
Solution Approach 1:
The computational analysis system performs self-service by automatically executing the complete workflow from image processing to correlation calculation without requiring manual intervention at each step. The system autonomously segments cells, calculates spatial densities, and determines correlation coefficients, thereby maintaining high measurement precision while simplifying operation for the user who only needs to initiate the analysis and interpret the results.
Solution Approach 2:
The system performs preliminary actions by pre-processing the stained tissue images through automated cell identification and density mapping before the actual correlation analysis. This preliminary computational work prepares the data structures and spatial information needed for accurate correlation measurement, reducing the operational burden on the user during the actual analysis phase.
3Measurement precision
If dual staining with specific antibodies is performed, then measurement precision is improved, but manufacturing precision requirements increase
Solution Approach 1:
The computational analysis system incorporates feedback mechanisms that evaluate the quality of staining patterns in the captured images. By analyzing the distribution, intensity, and morphology of stained cells, the system can detect variations in staining consistency and adjust processing parameters accordingly. This feedback loop maintains measurement precision even when manufacturing precision of the staining process varies, as the computational algorithms compensate for inconsistencies.
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
Enables precise measurement and visualization of correlations between cytotoxic T-cells and cancer cells, aiding in the development and assessment of cancer treatment drugs by providing a spatial analysis of cell densities and their interactions.
Implementation Method 1
The tissue slice on the slide is stained with a first antibody stain that is specific to a first protein present in a first type of cells, for example, CD8-positive cytotoxic T-cells. The tissue slice is stained with a second antibody stain that is specific to a second protein present in a second type of cells, for example, PDL1-positive cancer cells.
Data Source
AI summary
An analysis and visualization system analyzes a digital image of a tissue sample. In the sample, cells of a first type are stained in a first way, and cells of a second type are stained in a second way. The system segments the high-resolution image into first and second objects representing cells of the first and second types, respectively. The system also identifies a region of interest, and divides it into tiles. The system generates, for each tile, a first value and a second value. The first and second values for a tile are indicative of densities of the first and second objects in the tile. From the values, a measured correlation coefficient (CC) value is determined. The system compares the measured CC value to a reference CC value, thereby obtaining a correspondence value. The system then displays the image region along with a visualization of the correspondence value.