Virtual H&E Image Generation for Multiplex Pathology Analysis
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Solution Overview
Problem
Traditional multiplex image analysis in digital pathology requires additional H&E stained slides for cross-validation, leading to increased time, cost, and labor, and the spatial registration process is not fully accurate, complicating algorithm development.
Innovation Solution
Utilize generative AI models, such as cycleGAN, to transform multiplex images into virtual H&E images, allowing existing H&E domain tools to be applied directly for segmentation and analysis, eliminating the need for additional staining and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional multiplex image analysis uses additional H&E stained slides for cross-validation, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent creates a virtual H&E image as a copy of the multiplex image through domain transformation using generative adversarial networks. This virtual copy preserves the morphological information needed for cross-validation while eliminating the need for physical H&E staining, thereby maintaining measurement precision while significantly reducing time loss.
Solution Approach 2:
The patent performs domain transformation to generate virtual H&E images in advance, before the actual analysis process. This preliminary action prepares the cross-validation data structure ahead of time, allowing rapid analysis without the need for time-consuming physical staining and scanning of additional slides.
2Measurement precision
If traditional multiplex image analysis uses additional H&E stained slides for cross-validation, then measurement precision is improved, but loss of substance increases
Solution Approach 1:
The patent generates a virtual H&E image as a digital copy from the existing multiplex image data, eliminating the need to consume additional physical tissue samples for H&E staining. This digital copying approach maintains the ability to perform cross-validation while preserving valuable biological material.
Solution Approach 2:
The multiplex image itself serves dual purposes: it provides both the biomarker information and the morphological reference information needed for cross-validation. Through domain transformation, the single multiplex image generates its own virtual H&E counterpart, making the system self-sufficient without requiring additional tissue consumption.
3Measurement precision
If traditional multiplex image analysis uses spatial registration between H&E and multiplex slides, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent creates a virtual H&E image that is inherently aligned with the multiplex image through the domain transformation process. This eliminates the need for separate spatial registration steps, as the virtual copy maintains the same coordinate system and spatial relationships by definition, thereby reducing device complexity while maintaining alignment precision.
Solution Approach 2:
The patent merges the domain transformation and spatial alignment processes into a single unified operation. By transforming the multiplex image directly into virtual H&E domain while preserving spatial coordinates, the patent combines what were previously separate steps (staining, scanning, and registration) into one integrated process, reducing overall system complexity.
4Reliability
If traditional multiplex image analysis uses additional H&E stained slides for cross-validation, then reliability is improved, but productivity decreases
Solution Approach 1:
The patent generates virtual H&E images digitally from existing multiplex data, maintaining the reliability of cross-validation through accurate domain transformation while enabling parallel processing of multiple samples. This digital copying approach preserves analytical rigor while removing the sequential bottleneck of physical staining and scanning, thereby improving throughput.
Solution Approach 2:
The patent performs domain transformation and generates virtual H&E images as a preliminary step that can be done rapidly and parallelized across multiple samples. This preliminary digital preparation maintains reliability by establishing accurate morphological references before analysis, while enabling higher productivity through efficient batch processing capabilities.
Data Source
AI summary
The present disclosure relates to domain swap by accessing an image from a first domain and is processed using a machine learning model to generate a virtual synthetic image in a second domain. This approach can eliminate or reduce the need to separately collect an image in the second domain, which can save time and cost. Leveraging tools that are available in the second domain to perform image processing on the virtual synthetic image. Results or analysis from the image processing in the second domain can then be directly applied to the first domain and to assess the image further. Since spatial reference points (size, scale, view etc.) are same, pixels identifying a boundary of a region depicted in the virtual synthetic image are the same pixels in the first domain.