Tissue Image Proximity Scoring for Reproducible State Determination
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Solution Overview
Problem
Current image analysis methods for determining the biomedical state of tissue samples are limited in accurately and reproducibly quantifying spatial aspects of cell distributions, leading to subjective and inconsistent results across laboratories.
Innovation Solution
An image analysis method that computes a proximity score by comparing the observed relative distribution of A-type and B-type cells in a tissue sample with a reference distribution, integrating spatial differences and cell densities to provide a reproducible, quantitative measure of the biomedical state.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image analysis algorithms use protein expression levels to identify cells, then cell type identification is achieved, but accurate and reproducible determination of tissue biomedical state is insufficient
Solution Approach 1:
The patent transitions from analyzing only protein expression levels (one dimension) to incorporating spatial distribution information of cells (adding a second dimension). By computing proximity scores that measure distances between different cell types in tissue sections, the method captures spatial relationships that were previously ignored, thereby improving both accuracy and reproducibility of tissue state determination
Solution Approach 2:
The patent replaces subjective manual assessment methods with an automated computational approach. Image analysis algorithms automatically detect cell locations, compute spatial distributions, and generate proximity scores, eliminating inter-observer variability and enhancing reproducibility across different laboratories while maintaining measurement precision
2Loss of information
If multiple parameters of tumor-infiltrating lymphocytes are measured, then prognostic and predictive potential is enhanced, but spatial aspects of cell distributions are not adequately addressed
Solution Approach 1:
The patent extracts spatial distribution information from tissue images by detecting cell locations and computing distances between different cell types. This extracted spatial data is then integrated with existing multiple parameter measurements to create a comprehensive proximity score, capturing spatial aspects that were previously lost while maintaining analytical clarity
Solution Approach 2:
The proximity score computation framework serves multiple functions: it quantifies spatial distribution, identifies cell-cell interactions, and provides a reproducible metric for tissue state classification. This multi-functional approach efficiently captures spatial information without proportionally increasing analysis complexity
3Adaptability or versatility
If subjective methods are used for considering spatial relatedness, then flexibility in analysis is maintained, but ability to compare histopathological results across laboratories is hindered
Solution Approach 1:
The patent transforms subjective spatial assessment into an objective quantitative parameter (proximity score) based on measurable distances between cell centers. This parameter change from subjective evaluation to objective measurement enables consistent comparison across laboratories while preserving the ability to analyze different spatial configurations through the same standardized metric
Data Source
AI summary
An image analysis method for determining the biomedical state of a tissue sample. The method includes receiving a digital image of a tissue sample, identifying the number and location of A-type cells and B-type cells, obtaining an observed relative distribution, obtaining a reference relative distribution of expected distances between reference A-type cells and reference B-type cells, computing a proximity score as a difference of the reference relative distribution and the observed relative distribution, computing a combined score comprising the proximity score and the density of the A-type cells and/or the density of the B-type cells, and using the combined score for determining the biomedical state of the tissue sample and/or outputting the combined score.