Virtual Staining Model With Patch-Wise Alignment Confidence
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Solution Overview
Problem
The process of staining biological samples for cancer diagnosis is cumbersome and may not be feasible in locations where stains are not readily available or too expensive, and existing methods struggle with accurately aligning unstained and stained images for virtual staining.
Innovation Solution
A machine learning model is trained using image pairs of unstained and stained biological samples, employing patch-wise alignment techniques to ensure accurate alignment and generate virtually stained images, even with internal misalignments, by using alignment analysis systems to identify and deemphasize misaligned patches during training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If physical staining is used to enhance visibility of cellular features, then diagnostic accuracy is improved, but cost and complexity increase making it infeasible in resource-constrained settings
Solution Approach 1:
The patent creates a virtual copy of the stained image appearance by training a machine learning model to map unstained image features to stained image characteristics. The model learns the transformation from unstained to stained appearance and applies it to generate synthetic stained images, eliminating the need for physical stains while preserving diagnostic information.
Solution Approach 2:
The patent replaces the mechanical/chemical staining process with a computational image processing system. Instead of applying physical stains to tissue samples, the system uses a trained machine learning model to computationally generate stained-like images from unstained images, substituting chemical processes with algorithmic transformations.
2Measurement precision
If image alignment techniques are used to register unstained and stained images, then virtual staining accuracy is improved, but misaligned patches reduce training quality
Solution Approach 1:
The patent divides the image registration process into discrete patches that can be individually evaluated for alignment quality. By segmenting the images into multiple regions, the system can identify and exclude misaligned patches from training, ensuring that only high-quality aligned patches contribute to model learning.
Solution Approach 2:
The patent applies different quality thresholds and processing to different regions of the image based on local alignment quality. Misaligned patches are identified and either excluded or down-weighted in the training process, while well-aligned patches are used with full weight, allowing the model to learn from reliable data regions while ignoring problematic areas.
Data Source
AI summary
One example method for generating images of virtually stained biological samples includes receiving a first image pair comprising a first image of a biological sample and a second image of the biological sample, the first image captured using a first imaging technique and the biological sample being unstained, the second image captured using a second imaging technique different from the first imaging technique and the biological sample being stained; receiving a proposed alignment of the first image and the second image; generating alignment quality information corresponding to the first image pair, the alignment quality information indicating an alignment confidence of the first and second images; training a machine learning (“ML”) model, using the first image pair and the alignment quality information, to generate an output image of the biological sample having a virtual stain according to the second imaging technique from the first image; receiving, by the ML model, a first input image of a first biological sample captured using the first imaging technique, the first biological sample being unstained; and generating, by the ML model, a first output image according to the second imaging technique, the first output image comprising a virtually stained image of the first biological sample.