Virtual Staining Neural Network for Histology
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Solution Overview
Problem
Current methods for staining biological samples in histology are time-consuming, damage biomolecules, and do not provide real-time evaluation or detailed molecular information, hindering immediate feedback in critical settings like operating rooms.
Innovation Solution
A computer-implemented method using a trained artificial neural network to generate virtually stained images of unstained samples by accessing image training datasets and adjusting weights to predict the extent of stain binding, allowing for real-time evaluation and preservation of samples.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional staining methods are used to provide contrast and highlight tissue features, then measurement precision is improved, but loss of time increases and loss of substance occurs
Solution Approach 1:
The patent creates virtual copies of stained tissue images by training neural networks on pairs of unstained and stained images. The network learns to predict stained image characteristics from unstained images, generating synthetic stained images that replicate the appearance and diagnostic features of traditionally stained samples without requiring actual staining procedures.
Solution Approach 2:
The patent replaces the chemical staining process with a computational approach using machine learning. Instead of applying physical stains to tissue sections, the system uses neural networks to digitally simulate and generate virtual stained images from unstained multispectral images, substituting chemical processing with information processing.
2Measurement precision
If traditional staining methods are used to highlight cell types and tissue elements, then measurement precision is improved, but loss of substance occurs
Solution Approach 1:
The patent generates virtual copies of stained images that preserve all molecular information from the original unstained samples. By learning the relationship between unstained and stained appearances through neural networks, the system creates synthetic stained images that maintain the integrity of original biomolecules while providing the contrast and detail normally requiring chemical stains.
Solution Approach 2:
The patent substitutes chemical staining with computational image processing. Instead of using chemical dyes that can damage proteins and RNAs, the system uses machine learning algorithms to digitally enhance and simulate stained appearances, eliminating harmful chemical interactions while preserving molecular integrity.
3Device complexity
If conventional microscopy techniques are used to image tissue samples, then device complexity is reduced, but loss of time increases
Solution Approach 1:
The patent performs preliminary computational processing by training neural networks on large datasets of stained and unstained images before actual diagnosis is needed. This pre-training enables the system to rapidly generate virtual stained images in real-time during surgical procedures without requiring complex hardware or time-consuming staining processes.
Solution Approach 2:
The patent replaces time-consuming mechanical staining processes with rapid computational image generation. The neural network processes unstained multispectral images and outputs virtual stained images almost instantaneously, enabling real-time evaluation during surgery without the 30+ minute staining times required by conventional methods.
Data Source
AI summary
Systems and methods for predicting images with enhanced spatial resolution using a neural network are provided herein. According to an aspect of the invention, a method includes accessing an input image of a biological sample, wherein the input image includes a first spatial resolution and a plurality of spectral images, and wherein each spectral image of the plurality of spectral images includes data from a different wavelength band at a different spectral channel; applying a trained artificial neural network to the input image; generating an output image at a second spatial resolution, wherein the second spatial resolution is higher than the first spatial resolution, and wherein the output image includes a fewer number of spectral channels than the plurality of spectral images included in the input image; and outputting the output image.