Synthetic H&E Imaging From Multiplexed Immunofluorescence
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Solution Overview
Problem
Current multiplexed immunofluorescence (MPX) imaging techniques lack clear depiction of underlying tissue structure, making annotation and diagnosis challenging, and require additional H&E-stained images for reference, which is costly and time-consuming.
Innovation Solution
A computer-implemented method using a generator network, trained with paired MPX and histochemically stained images, generates synthetic images with high structural similarity to H&E stains by processing N-channel MPX images, leveraging autofluorescence and nuclear counterstain information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If MPX imaging is used to detect multiple biomarkers, then detection capability is improved, but tissue structure visibility deteriorates
Solution Approach 1:
The patent generates a synthetic H&E-stained image copy from the MPX image data, creating a virtual replica that replicates the appearance of traditional H&E staining. This synthetic copy contains the tissue structure information needed for diagnosis while the original MPX image preserves the biomarker detection capabilities, thus resolving the contradiction between multiplex detection and tissue structure visibility.
Solution Approach 2:
The patent introduces an intermediary processing step using a generator network that transforms MPX image data into synthetic H&E-like images. This intermediary transformation layer enables the system to simultaneously access both the biomarker information from MPX and the tissue structure information from the synthesized H&E appearance, eliminating the need to choose between the two.
2Measurement precision
If additional H&E-stained images are obtained for reference, then diagnostic accuracy is improved, but time consumption and cost increase
Solution Approach 1:
Instead of obtaining a physical H&E-stained reference slide through additional staining procedures, the patent creates a digital synthetic copy of the H&E appearance directly from the MPX image data. This virtual copy provides the necessary tissue structure reference for accurate diagnosis without requiring actual additional staining time or resources.
Solution Approach 2:
The patent performs preliminary computational transformation of the MPX image into a synthetic H&E appearance during the image processing workflow. By preparing the tissue structure reference in advance through algorithmic generation rather than requiring post-acquisition staining, the system eliminates time-consuming additional processing steps while maintaining diagnostic accuracy.
3Measurement precision
If additional H&E-stained images are obtained for reference, then diagnostic accuracy is improved, but cost increases
Solution Approach 1:
The patent replaces physical H&E staining resources with a computational synthesis process that generates virtual H&E images from MPX data. This eliminates the need for additional staining reagents, chemicals, and laboratory materials while still providing the necessary tissue structure information for accurate diagnosis, thus resolving the contradiction between diagnostic accuracy and resource consumption.
Solution Approach 2:
The patent substitutes the mechanical/chemical H&E staining process with a computational image processing system. Instead of using physical stains to visualize tissue structure, the system uses algorithmic generation based on MPX image data, replacing laboratory resources with computational resources that are more efficient and cost-effective.
Data Source
AI summary
Techniques for obtaining a synthetic histochemically stained image from a multiplexed immunofluorescence (MPX) image may include producing an N-channel input image that is based on information from each of M channels of an MPX image of a tissue section, where M and N are positive integers and N is less than or equal to M; and generating a synthetic image by processing the N-channel input image using a generator network, the generator network having been trained using a training data set that includes a plurality of pairs of images. The synthetic image depicts a tissue section stained with at least one histochemical stain. Each pair of images of the plurality of pairs of images includes an N-channel image, produced from an MPX image of a first section of a tissue, and an image of a second section of the tissue stained with the at least one histochemical stain.