Digital Staining System for Tumor Microenvironment Characterization
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Solution Overview
Problem
Conventional methods for analyzing tumor microenvironments from pathological images are impractical due to the manual labeling of millions of cells by pathologists, leading to resource inefficiency and inaccurate image segmentation, especially as they capture three-dimensional tissue structures as two-dimensional images, resulting in incomplete or inaccurate cell identification.
Innovation Solution
A histology-based digital staining system utilizing deep-learning and a Mask R-CNN architecture to segment and classify cell nuclei in pathology images, generating a characterized tumor microenvironment and a prognostic model for predicting patient prognosis and optimizing treatment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labeling of cells by pathologists is used to analyze tumor microenvironment, then diagnostic accuracy can be maintained, but resource consumption and time expenditure increase significantly
Solution Approach 1:
The patent introduces a deep learning-based image analysis system as an intermediary between pathology images and diagnostic conclusions. This automated system processes H&E stained tissue images to identify and characterize tumor microenvironment features, reducing the need for manual pathologist labeling while maintaining diagnostic accuracy through validated algorithms
Solution Approach 2:
The patent replaces the mechanical manual labeling process with an automated digital image analysis system. The system uses computational algorithms to automatically segment, detect, and characterize cells and tissue structures in pathology images, substituting human manual work with automated mechanical processing while preserving diagnostic quality
2Productivity
If conventional image segmentation techniques are used to automatically identify cells, then pathologist time is reduced, but segmentation accuracy becomes incomplete or inaccurate
Solution Approach 1:
The patent employs advanced deep learning architectures with optimized parameters and multi-scale feature extraction to improve segmentation accuracy. The system adjusts computational parameters and processing approaches to handle the complexity of tissue structures, achieving both high productivity and accurate cell identification simultaneously
3Ease of manufacture
If three-dimensional tissue structures are captured as two-dimensional images, then imaging simplicity is maintained, but cell identification accuracy deteriorates due to overlapping and touching cells
Solution Approach 1:
The patent addresses the 2D-to-3D mapping challenge by using multi-scale image analysis and spatial relationship reasoning. The system processes images at multiple magnification levels and uses contextual information from surrounding areas to accurately identify and separate overlapping cells, effectively recovering three-dimensional structural information from two-dimensional projections
Data Source
AI summary
Implementations discussed and claimed herein provide systems and methods for characterizing patient tissue of a patient. In one implementation, a pathological image of the patient tissue is received. Nuclei of the plurality of cells in the pathological image are simultaneously segmented and classified using a histology-based digital staining system. The nuclei of the plurality of cells are segmented according to spatial location and classified according to cell type, thereby generating one or more groups of nuclei. Each of the one or more groups of nuclei have an identified cell type. A composition and a spatial organization of a tumor microenvironment of the patient tissue is determined based on the one or more groups of nuclei. A prognostic model for the patient is generated based on the composition and the spatial organization of the tumor microenvironment.