Virtual Immunofluorescence Staining via Deep Learning
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Solution Overview
Problem
Conventional histopathology workflows for diagnosing diseases like cancer are time-consuming, expensive, and destructive to tissue samples, requiring chemical staining and extensive expertise, which limits their efficiency and consistency.
Innovation Solution
The application of deep learning-based virtual immunofluorescence staining techniques, which generate synthetic images of tissue slides with predicted staining outcomes, allowing for non-destructive, fast, and cost-effective visualization of multiple biomarkers in a single image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional chemical staining techniques are used to visualize biomarkers in tissue samples, then pathologists can identify disease features and diagnose conditions, but the process becomes time-consuming, expensive, and destructive to the tissue samples
Solution Approach 1:
The patent uses deep learning models to generate synthetic stained images that replicate the appearance of conventional immunofluorescence staining without requiring actual chemical staining. The model learns the mapping between H&E stained images and corresponding IF stained images, then generates virtual IF images that preserve tissue morphology while predicting biomarker locations, thereby avoiding time-consuming chemical processes while maintaining diagnostic accuracy
Solution Approach 2:
The patent replaces the mechanical and chemical staining process with a computational deep learning system. Instead of using chemical reagents and physical staining procedures, the system uses trained neural networks to predict and generate synthetic stained images, substituting a computational mechanism for the traditional chemical-mechanical process
2Measurement precision
If conventional chemical staining techniques are used to visualize biomarkers in tissue samples, then pathologists can identify disease features and diagnose conditions, but the process becomes expensive and requires extensive expertise
Solution Approach 1:
The deep learning model creates accurate copies of the staining appearance and biomarker visualization without requiring the complex chemical staining process. The model captures the essential visual patterns of IF staining from training data and reproduces them synthetically, eliminating the need for expensive chemical reagents and specialized staining equipment while maintaining diagnostic quality
Solution Approach 2:
The patent substitutes the complex chemical staining system with a computational deep learning system. The trained model performs the function of biomarker visualization through algorithmic processing rather than chemical reactions, reducing both the material complexity and operational complexity of the staining process
3Adaptability or versatility
If conventional chemical staining techniques are used to visualize multiple biomarkers, then comprehensive disease analysis is possible, but multiple separate staining processes are required which increases time and resource consumption
Solution Approach 1:
The deep learning model is trained to generate multiple types of immunofluorescence stains simultaneously from a single H&E stained image. The universal model can predict different biomarker patterns (e.g., PD-L1, CD8, CD45) in parallel, allowing comprehensive multimarker analysis from one input image without requiring separate staining procedures for each biomarker
Data Source
AI summary
Example systems and methods for generating virtual immunofluorescence stains for tissue samples are provided. A computing device receives a slide image of a target tissue sample of a particular tissue type. The computing device selects a first trained machine learning (ML) model to generate virtual immunofluorescence (IF) stains of a first type for the particular tissue type based on a user input. The first trained ML model is trained at least based on a first set of stain images of a plurality of training tissue samples with stains of the first type. The computing device generate a virtually stained image of the target tissue sample with the virtual IF stains of the first type using the first trained ML model. The computing device displays the virtually stained image of the target tissue sample with the virtual IF stains of the first type.