Virtual Staining via Segmented Neural Networks
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Solution Overview
Problem
Current virtual staining techniques in microscopy produce images of moderate quality due to errors in chemically stained training data, which are difficult to correct manually, and do not reliably distinguish subcellular structures like DNA locations within cells.
Innovation Solution
A computer-implemented method for generating an image processing model that calculates virtually stained images from microscope images using training data comprising microscope images and chemically stained images with predefined segmentation masks to correct errors and optimize a staining reward/loss function, allowing for high-quality image generation without chemical staining.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If chemical staining methods are used to improve visibility of certain structures, then the visibility of cell organelles and tissue structures is improved, but the sample is subjected to considerable stress and additional harmful effects occur
Solution Approach 1:
The patent creates a virtual copy of the staining effect through image processing. A trained neural network model generates a virtually stained image that replicates the appearance of chemically stained structures without actually applying stains to the sample. This copying approach achieves the visibility enhancement goal while completely avoiding the harmful effects of chemical staining and prolonged illumination on the biological sample
Solution Approach 2:
The patent replaces the mechanical/chemical staining process with a computational image processing system. Instead of using physical dyes and fluorophores to stain structures, the system uses a trained neural network that processes the phase-contrast or other unstained microscope images to generate a virtually stained image, substituting chemical mechanisms with computational algorithms
2Measurement precision
If chemical staining methods are used to make structures visible, then certain cell organelles become more visible, but reliability decreases due to bleed-through and accidental staining
Solution Approach 1:
The virtual staining approach creates a faithful copy of the staining effect through computational methods. The neural network learns the relationship between unstained and stained images during training, then applies this learned transformation to generate accurate virtual stains. This copying process eliminates reliability issues like bleed-through and accidental staining because no physical chemicals are involved in the staining process
Solution Approach 2:
The patent introduces an image processing model as an intermediary between the raw microscope image and the final stained image. This intermediary neural network acts as a intelligent translator that converts phase-contrast or other unstained images into virtually stained images, eliminating the need for direct chemical interaction and its associated reliability problems
3Measurement precision
If transfection staining is used to stain specific DNA or RNA sequences, then specific structures become visible, but the transfection rate does not reach 100% resulting in erroneous non-visibility
Solution Approach 1:
The virtual staining method creates a complete copy of the staining effect for all structures visible in the phase-contrast image. Since the neural network learns from training data that includes all stained structures, it can generate virtual stains for 100% of the visible structures in the input image, eliminating the incomplete coverage problem of transfection staining where some cells fail to express fluorophores
4Object-affected harmful factors
If virtual staining techniques are used to avoid sample stress, then sample stress is reduced, but image quality is moderate due to errors in chemically stained training data
Solution Approach 1:
The patent segments the training data into distinct components: phase-contrast images, chemically stained images, and segmentation masks. The segmentation masks divide the stained images into regions corresponding to specific structures. By training the neural network to map phase-contrast images to these segmented regions, the system learns accurate structure boundaries and staining patterns, improving image quality while maintaining the stress-free advantage of virtual staining
Solution Approach 2:
The patent implements a feedback mechanism during training where the neural network's predictions are compared against ground truth stained images and segmentation masks. The loss function calculates the difference between predicted and actual stained regions, providing feedback that guides the network to improve its predictions. This feedback loop enables the system to learn from training data and generate high-quality virtual stained images that accurately represent the sample structures
Data Source
AI summary
A computer-implemented method for generating an image processing model (M) that calculates a virtually stained image (30) from a microscope image (20) comprises a training (15) of the image processing model (M) using training data (T) comprising at least: microscope images (20) as input data into the image processing model (M); target images (50) formed using captured chemically stained images (60); and predefined segmentation masks (70) that discriminate between image regions (71, 72) to be stained and image regions (72) that are not to be stained. The image processing model (M) is trained to calculate virtually stained images (30) from the input microscope images (20) by optimizing a staining reward/loss function (LSTAIN) that captures a difference between the virtually stained images (30) and the target images (50). The predefined segmentation masks (70) are taken into account in the training (15) of the image processing model (M) to compensate errors in the chemically stained images (60).