Machine Learning Virtual Staining from H&E Tissue Images
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Solution Overview
Problem
The high cost and limited availability of special stains like IHC stains, along with the time-consuming process of obtaining multiple stain images, pose challenges in histological tissue analysis, particularly in cases where additional biopsies are required.
Innovation Solution
A machine learning predictor model is trained using aligned pairs of images, one stained with H&E and one with a special stain, to generate a virtual image of the special stain from an input image, reducing the need for actual staining by predicting the appearance of special stains like IHC stains.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If actual special staining is performed to obtain multiple stain images, then diagnostic accuracy is improved, but cost increases and time consumption increases
Solution Approach 1:
The patent creates virtual copies of special stained tissue images by training a machine learning model on pairs of H&E stained images and actual special stain images. The model learns to generate synthetic special stain images from H&E images, allowing pathologists to obtain multiple virtual stain images from a single physical tissue section without performing multiple actual staining procedures.
Solution Approach 2:
The patent replaces expensive and time-consuming actual special staining procedures with computationally generated virtual stain images. The virtual images are produced at minimal cost through machine learning inference, eliminating the need for repeated physical staining operations while maintaining diagnostic utility.
2Reliability
If actual special staining is performed to obtain multiple stain images, then diagnostic accuracy is improved, but cost increases
Solution Approach 1:
The patent generates virtual copies of special stained images through machine learning, replacing the need for multiple expensive physical staining procedures. The system trains on paired H&E and special stain images to create synthetic special stain images at minimal cost, significantly reducing the financial burden of obtaining multiple stain types.
Solution Approach 2:
The patent substitutes costly actual special staining operations with low-cost virtual image generation. The machine learning model produces virtual stain images through computational processing, eliminating the need to purchase and apply multiple expensive stain reagents while maintaining diagnostic accuracy.
3Reliability
If multiple actual stain images are obtained, then comprehensive diagnostic information is improved, but tissue availability is reduced
Solution Approach 1:
The patent creates virtual copies of multiple special stain images from a single H&E stained tissue section. The machine learning model generates synthetic images representing different stain types (IHC, PAS, GMS, etc.) from the same input H&E image, allowing comprehensive diagnostic evaluation without requiring multiple separate tissue sections.
Solution Approach 2:
The patent enables a single H&E stained tissue section to serve multiple diagnostic functions by generating virtual images for various stain types. The machine learning system can produce different virtual stain images from the same input, making the single tissue section universally applicable for multiple diagnostic purposes without consuming additional tissue.
4Reliability
If multiple actual stain images are obtained, then comprehensive diagnostic information is improved, but turnaround time increases
Solution Approach 1:
The patent performs preliminary action by generating virtual special stain images during or after the H&E staining process, before the tissue is fully processed or before the pathologist needs the information. The machine learning model can rapidly generate multiple virtual stain images from the H&E image, providing diagnostic information ahead of time without waiting for multiple sequential physical staining procedures.
Solution Approach 2:
The patent replaces the mechanical and chemical process of actual special staining with a computational machine learning system. Instead of physically applying multiple stain reagents through sequential mechanical processes, the system uses neural network inference to generate virtual stain images rapidly, dramatically reducing the time required to obtain multiple diagnostic images.
Data Source
AI summary
A machine learning predictor model is trained to generate a prediction of the appearance of a tissue sample stained with a special stain such as an IHC stain from an input image that is either unstained or stained with H&E. Training data takes the form of thousands of pairs of precisely aligned images, one of which is an image of a tissue specimen stained with H&E or unstained, and the other of which is an image of the tissue specimen stained with the special stain. The model can be trained to predict special stain images for a multitude of different tissue types and special stain types, in use, an input image, e.g., an H&E image of a given tissue specimen at a particular magnification level is provided to the model and the model generates a prediction of the appearance of the tissue specimen as if it were stained with the special stain. The predicted image is provided to a user and displayed, e.g., on a pathology workstation.