Artificial IHC Image Generation from H&E Brightfield Microscopy
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Solution Overview
Problem
Current methods for analyzing tumor organoids, such as fluorescence microscopy, are time-consuming, costly, and can be toxic to cells, leading to inaccurate results, necessitating a more efficient and non-toxic approach for monitoring cell viability and drug effectiveness.
Innovation Solution
A system and method that uses trained models to generate artificial fluorescent images from raw brightfield images, allowing for the automatic analysis of tumor organoids without the need for fluorescent dyes or microscopy, by applying machine learning techniques to identify cell viability and morphology.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fluorescent microscopy is used to detect cell viability, then measurement precision is improved, but loss of time increases and object-affected harmful factors increase
Solution Approach 1:
The system performs preliminary staining of cells with fluorescent dyes before imaging, allowing the staining process to be completed in advance. This enables faster imaging acquisition since the cells are pre-prepared and ready for immediate observation, reducing the total analysis time while maintaining detection accuracy.
Solution Approach 2:
The system creates multiple image copies through automated high-throughput imaging, capturing numerous fields of view and cell images simultaneously. This copying approach allows parallel processing and analysis of large numbers of cells, significantly increasing productivity without sacrificing measurement precision.
2Measurement precision
If fluorescent microscopy is used to detect cell viability, then measurement precision is improved, but object-affected harmful factors increase
Solution Approach 1:
The system uses disposable, non-toxic alternative staining methods or label-free imaging techniques that do not require persistent fluorescent dyes. These temporary or mild staining approaches allow cell viability detection without the long-term toxic effects of traditional fluorescent markers, maintaining measurement accuracy while reducing harm to living cells.
Solution Approach 2:
The system changes the imaging parameters by using different wavelengths, lower intensity illumination, or alternative optical contrast methods that reduce phototoxicity. By adjusting these parameters, the system maintains sufficient measurement precision while minimizing the harmful effects of fluorescent illumination on cell viability.
3Measurement precision
If fluorescent microscopy is used to detect cell viability, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system uses a single imaging platform that can perform multiple functions: brightfield imaging, fluorescent imaging, and automated image analysis. This multi-functional approach eliminates the need for separate specialized equipment for different imaging modes, reducing overall device complexity while maintaining high measurement precision through integrated capabilities.
Solution Approach 2:
The system incorporates automated image acquisition, processing, and analysis capabilities that reduce the need for manual intervention and complex operational procedures. The automated algorithms automatically adjust imaging parameters, process images, and generate results, simplifying the user interface and operational complexity while maintaining measurement accuracy.
4Productivity
If automated image analysis is used instead of fluorescent microscopy, then productivity is improved, but measurement precision may worsen
Solution Approach 1:
The system uses feedback mechanisms where automated image analysis results are continuously validated and refined. The software learns from validated results and adjusts its algorithms to improve accuracy, ensuring that high-throughput automated analysis maintains measurement precision comparable to manual fluorescent microscopy methods.
Solution Approach 2:
The system replaces manual fluorescent microscopy operations with automated computational image analysis. Machine learning algorithms and computer vision techniques substitute for manual cell counting and viability assessment, enabling high-throughput processing while maintaining or improving measurement precision through consistent, objective automated measurements.
Data Source
AI summary
The disclosure provides a method of generating an artificial immunohistochemistry (IHC) image of cells. The method includes receiving a hematoxylin and eosin (H&E) stained whole slide image (WSI) generated by a brightfield microscopy imaging modality of at least a portion of cells included in a specimen, applying, to the H&E brightfield image, at least one trained model, the trained model being trained to generate the artificial IHC image based on the H&E brightfield image, receiving the artificial IHC image from the trained model.