Deep Learning Model for Virtual Immunofluorescence Image Translation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for determining the spatially-resolved molecular profile of cancer are time-consuming, resource-intensive, and costly, limiting their accessibility and effectiveness, especially in low-income communities.
Innovation Solution
A deep learning-based method called SHIFT (Speedy Histological-to-IF Translation) uses images of biological samples stained with H&E to generate estimated images of the same samples stained with immunofluorescence (IF) techniques, allowing for the creation of virtual IF images quickly and at a lower cost.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If immunofluorescence (IF) or immunohistochemistry (IHC) staining is used to determine spatially-resolved molecular profile, then measurement precision is improved, but loss of time and productivity deteriorate
Solution Approach 1:
The patent creates virtual immunofluorescence images by training a deep learning model on paired H&E and IF images. The model learns to generate IF-like images from H&E inputs, effectively copying the molecular profile information without requiring actual IF staining. This allows rapid generation of molecular profiles from routinely stained slides.
Solution Approach 2:
The patent transforms the staining technique parameter from immunofluorescence to hematoxylin and eosin staining. By changing the staining method that pathologists use, the system eliminates time-consuming IF procedures while maintaining the ability to extract spatially-resolved molecular profiles through computational methods.
2Measurement precision
If immunofluorescence (IF) or immunohistochemistry (IHC) staining is used to determine spatially-resolved molecular profile, then measurement precision is improved, but productivity deteriorates
Solution Approach 1:
The deep learning model generates virtual IF images that replicate the molecular profile information obtainable through actual IF staining. This copying approach enables rapid assessment of multiple samples without requiring time-consuming wet lab procedures for each sample.
Solution Approach 2:
The patent performs preliminary action by pre-training the deep learning model on a dataset of paired H&E and IF images. Once trained, the model can rapidly generate molecular profiles for new samples without requiring actual IF staining, thus improving productivity while maintaining measurement precision.
3Measurement precision
If immunofluorescence (IF) staining is used for detailed molecular assessment, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The system creates virtual IF images that replicate the molecular information obtained through specialized IF staining. This computational copying eliminates the need for expensive specialized hardware and reagents, making molecular profile assessment accessible with standard H&E staining equipment.
Solution Approach 2:
The patent replaces expensive, resource-intensive IF staining reagents and specialized imaging hardware with a computational model that runs on standard computing infrastructure. This substitution with cheaper alternatives maintains measurement precision while reducing device complexity and cost.
4Measurement precision
If immunofluorescence (IF) staining is used for comprehensive molecular assessment, then measurement precision is improved, but loss of substance and resource consumption worsen
Solution Approach 1:
The deep learning model generates virtual IF images that replicate molecular profile information without consuming expensive IF reagents. This computational copying approach maintains measurement precision while eliminating resource consumption associated with actual immunostaining procedures.
Solution Approach 2:
The patent substitutes expensive IF reagents with a computational model that requires minimal physical resources. This replacement with cheaper alternatives maintains the ability to accurately characterize molecular profiles while dramatically reducing substance consumption.
Data Source
AI summary
Techniques and systems for translating images of biological samples stained according to a first staining technique into images representing the biological samples stained according to a second staining technique. In various implementations, the first staining technique can include a histopathological staining technique and the second staining technique can include an immunofluorescence staining technique or an immunohistochemistry staining technique.