Artifact Detection in Virtual Histology Staining
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Solution Overview
Problem
Virtual histology staining methods often introduce artifacts due to non-unique mapping of grayscale images to color, leading to incorrect diagnoses, as they may create morphological structures not physically present in the tissue.
Innovation Solution
A computer-implemented method generates additional images based on either the initial or virtually stained histology images to identify differences, using pixel-by-pixel comparisons, optical flow, or neural networks to create a mapping of these differences, allowing for the identification and masking of potential artifacts in the virtually stained images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If virtual staining is performed using deep learning algorithms to convert grayscale images to color images, then staining efficiency and consistency are improved, but artifacts may be introduced due to non-unique mapping from grayscale to color
Solution Approach 1:
The patent performs preliminary actions by generating multiple candidate color images from the grayscale image using different deep learning models or multiple runs, before selecting the final stained image. This preliminary generation of multiple options allows for subsequent comparison and validation, preventing artifact introduction at the source
Solution Approach 2:
The patent implements feedback mechanisms by comparing the virtually stained image with the original grayscale image and potentially with ground truth stained images to identify and locate artifacts. The system uses this feedback information to flag or correct problematic regions, maintaining diagnostic accuracy while preserving staining efficiency
2Device complexity
If a one-to-one mapping is attempted from grayscale to color in virtual staining, then the process is simplified, but morphological artifacts are introduced because the mapping is not unique
Solution Approach 1:
The patent segments the staining process by handling different tissue structures separately through multiple deep learning models or multiple processing passes. Each model can be specialized for specific tissue types, allowing accurate color assignment without introducing artifacts from forced one-to-one mapping
Solution Approach 2:
The patent changes parameters by using multiple deep learning models with different training configurations or by adjusting mapping parameters iteratively. This allows the system to find optimal color mappings for different regions, maintaining morphological accuracy while avoiding the oversimplification of a single fixed mapping rule
3Measurement precision
If multiple deep learning models are used to generate candidate stained images for comparison, then artifact detection accuracy is improved, but computational time and resources increase
Solution Approach 1:
The patent applies partial action by using multiple deep learning models selectively rather than always. For example, it may use a lightweight model for quick initial staining and only invoke heavier models or comparison procedures when artifacts are suspected or in critical diagnostic regions, balancing accuracy with time efficiency
Solution Approach 2:
The patent performs preliminary artifact detection using a fast method or simplified comparison, and only proceeds to more computationally intensive multi-model comparison when initial detection suggests potential artifacts. This staged approach maintains high detection accuracy while minimizing overall processing time
Data Source
AI summary
A computer-implemented method for locating possible artifacts in a virtually stained histology image, which was obtained from an initial histology image, is provided. In the method, at least one further image is generated on the basis of the virtually stained histology image or the initial histology image. The differences between the at least one further image and the virtually stained histology image are determined when the at least one further image has been generated on the basis of the initial histology image or the differences between the at least one further image and the initial histology image are determined when the at least one further image has been generated on the basis of the virtually stained histology image, and a mapping from the determined differences to their positions in the virtually stained histology image is created. The positions in the virtually stained histology image represent the positions of possible artifacts.