Cellular Staining Analysis with Dynamic Neighborhood Segmentation
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Solution Overview
Problem
Current algorithms for analyzing histochemical or cytological samples with complex staining patterns are inadequate as they often miss nuclei or fail to detect membrane staining outside predefined neighborhoods, leading to incomplete or incorrect quantification, especially when multiple staining compartments are intermixed.
Innovation Solution
An improved image analysis system that segments digital images into distinct regions based on analyte staining patterns, generates analyte intensity maps, and identifies analytically relevant portions within compound staining regions to quantify analyte staining accurately, even in regions with intermixed staining patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If algorithms use predefined neighborhood radius around nuclei to detect membrane staining, then detection process is simplified, but staining outside the predefined neighborhood is missed
Solution Approach 1:
The algorithm dynamically adjusts the neighborhood radius for each nucleus based on the actual distance to the membrane, rather than using a fixed predefined radius. This allows the detection region to adapt to varying cell sizes and membrane positions, ensuring complete membrane staining detection while maintaining computational efficiency.
2Device complexity
If algorithms focus on single compartment staining detection, then analysis is simpler, but intermixed staining patterns are ignored
Solution Approach 1:
The algorithm segments the staining pattern into distinct biological compartments (membrane, cytoplasm, nucleus) and analyzes each compartment separately. By identifying characteristic staining patterns for each compartment and processing them independently, the system achieves accurate quantification of intermixed staining patterns without excessive computational complexity.
3Productivity
If algorithms use fixed detection regions around nuclei, then processing speed is maintained, but staining in variable locations is missed
Solution Approach 1:
The detection region is dynamically defined based on the actual spatial relationship between nuclei and membrane staining, rather than using fixed geometric shapes. The algorithm identifies membrane staining at variable distances from nuclei by adapting the search region to each cell's morphology, ensuring complete detection while maintaining processing efficiency through optimized search strategies.
Data Source
AI summary
Systems and methods discussed herein include, among other things, a method comprising quantifying analyte staining of a biological compartment in a region in which said staining is intermixed with analyte staining of an analytically-distinct distinct biological compartment. Disclosed systems and methods include, for example, a system and method for identifying membrane staining of an analyte of interest in regions where diffuse membrane staining is intermixed with cytoplasmic staining and/or punctate staining is disclosed. Disclosed systems and methods include, for example, a system and method for quantifying membrane staining of an analyte of interest in tissue or cytological samples having regions in which membrane staining is intermixed with cytoplasmic staining and/or punctate staining.