Multi-Modal Virtual Staining Through Machine-Learning Fusion
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Solution Overview
Problem
Existing chemical staining techniques in histopathology are labor-intensive and costly, requiring multiple tissue samples for each stain and different protocols, limiting efficient and accurate diagnosis.
Innovation Solution
A method utilizing machine-learning logic to fuse and process multiple sets of imaging data acquired using various imaging modalities, generating output images with virtual stains that mimic chemical stains, allowing for accurate and flexible virtual staining of tissue samples.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple chemical stains are applied to tissue samples to fully assess pathology cases, then diagnostic accuracy is improved, but labor intensity and cost increase significantly
Solution Approach 1:
The patent creates virtual copies of chemical stain effects through machine learning models. The system trains neural networks on paired datasets of unstained and chemically stained images, enabling the model to generate synthetic stained images that replicate the visual appearance of actual chemical stains without requiring physical staining processes. This copying approach eliminates the need for multiple physical staining procedures while maintaining diagnostic quality
Solution Approach 2:
The patent replaces the mechanical and chemical staining process with a computational system. Instead of using physical chemicals, reagents, and manual staining protocols, the system uses machine learning algorithms processed through computer hardware to generate stained images. This substitution transforms a labor-intensive wet lab process into an automated digital workflow, dramatically reducing hands-on time and material costs
2Reliability
If multiple tissue samples are prepared for different chemical stains, then comprehensive diagnosis is improved, but sample quantity and processing complexity increase
Solution Approach 1:
The patent creates a universal machine learning model that can generate multiple different virtual stain types from a single input image. The trained system can produce H&E stains, immunohistochemical stains, and other stain variations without requiring separate processing pipelines for each stain type. This multi-functionality allows comprehensive diagnostic assessment from a single tissue sample preparation
Solution Approach 2:
The patent merges multiple staining protocols and their associated processing steps into a single computational workflow. By combining the functionality of multiple stain-specific models into one unified system, the patent eliminates the need for separate sample preparations and processing sequences, reducing overall complexity while maintaining the ability to generate diverse diagnostic information
3Measurement precision
If conventional chemical staining procedures are used, then tissue structure visualization is improved, but time consumption and cost increase
Solution Approach 1:
The patent performs preliminary training of machine learning models on extensive datasets of stained and unstained images before actual diagnostic use. This pre-training phase captures the complex relationships between tissue structures and their stained appearances, so that during actual diagnosis, the system can rapidly generate stained images without undergoing the time-consuming chemical staining process. The preliminary computational work enables fast real-time or near-real-time visualization
Solution Approach 2:
The patent replaces time-consuming chemical reactions, washing steps, and drying procedures with instantaneous computational image processing. The machine learning model generates stained images through mathematical operations that complete in seconds or minutes, compared to the hours required for conventional staining protocols, while maintaining or improving visualization quality
Data Source
AI summary
A method of virtual staining of a tissue sample includes obtaining multiple sets of imaging data. The imaging data depicts a tissue sample and has been acquired using multiple imaging modalities. Further, the method includes fusing and processing the multiple sets of imaging data in a machine-learning logic. The machine-learning logic is configured to provide at least one output image. Each one of the at least one output image depicts the tissue sample comprising a respective virtual stain.