Tissue Area Normalization Algorithm for Digital Image Analysis
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Solution Overview
Problem
Current digital image analysis methods for tissue sections are inadequate in accurately determining tissue area, often including non-tissue regions and failing to distinguish between tissue objects and non-tissue clusters, which limits the accuracy of biomarker expression quantification.
Innovation Solution
The method involves using an algorithmic process to extract morphometric, staining, and localization features to define tissue area by classifying tissue objects and clusters, enabling precise determination of target tissue area for normalization in digital image analysis of whole slide tissue sections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If current digital image analysis tools determine tissue area based on individual pixel intensity thresholds, then the analysis can be automated and processed quickly, but the tissue area determination becomes inaccurate by including non-tissue regions and failing to distinguish between tissue objects and non-tissue clusters
Solution Approach 1:
The patent segments the tissue image into distinct tissue objects and non-tissue regions by analyzing spatial relationships and neighborhood characteristics between pixels, rather than relying solely on individual pixel intensity thresholds. This segmentation approach enables accurate differentiation between tissue structures (cells, glands, vessels) and non-tissue elements (clear glass, artifacts) while maintaining automated processing capabilities.
Solution Approach 2:
The patent introduces an intermediary analysis layer that examines the neighborhood relationships and spatial context between pixels to mediate between raw pixel data and tissue area determination. This intermediary step evaluates local features and object clusters to accurately identify tissue regions, resolving the contradiction between automated processing and measurement precision.
2Adaptability or versatility
If manual annotations are used to define tissue area, then the analysis can be customized to specific regions of interest, but the annotation process becomes time-consuming and labor-intensive
Solution Approach 1:
The patent enables the image analysis system to self-define tissue areas by automatically analyzing image features, object clusters, and spatial relationships without requiring manual annotation. The system autonomously identifies tissue regions of interest based on predefined criteria and neighborhood characteristics, eliminating the time-consuming manual annotation process while maintaining the ability to customize analysis to specific biological contexts.
3Device complexity
If color or color intensity thresholds are used to distinguish tissue from clear glass, then the method is simple to implement, but it cannot accurately distinguish between tissue objects and non-tissue objects with similar staining characteristics
Solution Approach 1:
The patent transitions from two-dimensional color intensity analysis to a multi-dimensional analysis that incorporates spatial relationships, neighborhood characteristics, and object cluster properties. By adding the dimension of spatial context and object-level features, the system achieves accurate differentiation between tissue objects and non-tissue elements while maintaining reasonable implementation complexity through algorithmic approaches.
Data Source
AI summary
Staining of tissue is a common approach utilized to visualize a gene product in tissue context. In certain applications, it is necessary to report a sum of events within the tissue as a specific function of the target tissue area, which is a sub-area of the total tissue, as a normalization factor for reporting the quantification. Here, we describe methods of determining target tissue area and reporting a quantification which is ratiometric to the target tissue area, utilizing computer algorithms. It is important to assign a value for the “target tissue area” in scenarios where a tissue area normalization factor is needed in the most pathologically relevant fashion during the application of tissue image analysis. We have created methods for determining and reporting “target tissue area” as normalization factor which are useful in diagnostic applications utilizing image analysis.