Tissue Image Segmentation for Intratumor Spatial Heterogeneity Mapping
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Solution Overview
Problem
Current digital pathology workflows for analyzing H&E stained tissue images and multiplexed/hyperplexed fluorescence tissue images are time-consuming, error-prone, and lack effective methods to quantify intratumor cellular spatial heterogeneity, particularly in characterizing the spatial relationships between various histological structures and biomarker patterns.
Innovation Solution
A method and system that utilize opponent color space normalization and graph-based spectral segmentation to segment H&E stained images, and quantify intratumor cellular spatial heterogeneity in fluorescence images by employing pointwise mutual information and biomarker intensity patterns to identify and analyze spatial associations between cells.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual assessment of cellular spatial distribution is performed by pathologists, then diagnostic accuracy can be maintained, but the process is time-consuming and subjective variability increases
Solution Approach 1:
The patent replaces manual visual assessment by pathologists with automated computational image analysis systems. The system uses digital imaging technology, image processing algorithms, and computer-based quantification methods to objectively measure cellular spatial distribution, replacing the mechanical human eye and brain analysis process while maintaining diagnostic accuracy and eliminating subjectivity.
Solution Approach 2:
The patent transforms qualitative pathological assessment into quantitative measurements by defining specific parameters for cellular spatial distribution analysis. The system quantifies parameters such as cell density, spatial clustering, and distribution patterns, converting subjective visual evaluation into objective numerical data that can be precisely measured and compared.
2Productivity
If automated image analysis systems are implemented, then processing speed and objectivity improve, but system complexity and development requirements increase
Solution Approach 1:
The patent describes a multi-functional integrated system that combines digital image acquisition, preprocessing, segmentation, feature extraction, and quantification capabilities within a single platform. The system can analyze multiple tissue types and cellular features using the same core architecture, reducing overall system complexity through functional integration rather than requiring separate specialized systems for each analysis task.
Solution Approach 2:
The patent divides the complex image analysis process into distinct modular stages: image acquisition, preprocessing, segmentation, feature extraction, and quantification. Each stage is handled by separate computational modules that can be independently optimized and validated, making the overall complex system more manageable through functional decomposition.
3Illumination intensity
If traditional H&E staining is used, then tissue morphology can be visualized, but cellular spatial heterogeneity and molecular information cannot be quantified
Solution Approach 1:
The patent combines traditional H&E staining with multiplexed fluorescence imaging techniques to create an integrated analysis system. The system processes both the morphological information from H&E stains and the molecular information from fluorescence markers simultaneously, merging multiple information sources into a unified quantitative analysis framework that preserves both tissue architecture and molecular heterogeneity data.
Data Source
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AI summary
Graph-theoretic segmentation methods for segmenting histological structures in H&E stained images of tissues. The method relies on characterizing local spatial statistics in the images. Also, a method for quantifying intratumor spatial heterogeneity that can work with single biomarker, multiplexed, or hyperplexed immunofluorescence (IF) data. The method is holistic in its approach, using both the expression and spatial information of an entire tumor tissue section and/or spot in a TMA to characterize spatial associations. The method generates a two-dimensional heterogeneity map to explicitly elucidate spatial associations of both major and minor sub-populations.