Synthetic IHC Digital Slides with Controlled Ground Truth
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Solution Overview
Problem
Existing immunohistochemistry (IHC) stain interpretation in clinical practice suffers from poor reproducibility due to manual enumeration and inter- and intra-observer variability, and current synthetic histopathological images lack realism, making it difficult to validate analytical methods effectively.
Innovation Solution
Utilizing a conditional Generative Adversarial Network (cGAN) to generate realistic histopathological images with controlled ground truth, allowing for the creation of virtual tissue sections with known positive and negative cell proportions and distributions, mimicking real human cells and structural inconsistencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual enumeration is used for IHC stain interpretation, then pathologists can perform quantitative analysis, but reproducibility deteriorates due to inter- and intra-observer variability
Solution Approach 1:
The patent creates synthetic digital slides that copy the essential features of real IHC-stained tissue sections while providing ground truth annotations. These synthetic slides replicate staining patterns, cell morphologies, and tissue architectures to enable reliable evaluation of quantitative image analysis methods without relying on manual pathologist enumeration.
Solution Approach 2:
The patent replaces manual mechanical enumeration by pathologists with computational image analysis systems. The synthetic slides are designed to be evaluated by automated algorithms, replacing the mechanical process of manual cell counting and staining intensity assessment with digital image processing and machine learning methods.
2Reliability
If multiple experts are involved in generating reference standards, then measurement reliability improves, but evaluation process becomes resource-intensive and sample size is limited
Solution Approach 1:
The synthetic slide generation system is self-service in that it automatically creates ground truth annotations without requiring multiple expert pathologists. The neural network models generate consistent, reproducible annotations that serve as reliable reference standards, eliminating the need for expert consensus processes and significantly increasing evaluation efficiency.
3Loss of information
If traditional synthetic histopathological images are generated, then ground truth is known, but image realism deteriorates making validation difficult
Solution Approach 1:
The patent replaces traditional rule-based synthetic image generation with deep neural network models that learn from real IHC-stained tissue sections. The neural networks capture complex staining patterns, cellular structures, and tissue architectures, generating synthetic images that are visually indistinguishable from real sections while maintaining precise ground truth annotations.
Data Source
AI summary
Disclosed herein are systems, methods and computer-program products to create synthetic immunohistochemistry (IHC) stained digital slides or virtual tissue sections generated using artificial neural networks (ANNs). In some implementations, the created digital slides or a virtual tissue sections can be used as a ground truth to evaluate a method of analyzing IHC stained tissues.