Tissue Image Color Deconvolution Using Gaussian Prior Stain Variability
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Solution Overview
Problem
Conventional color deconvolution methods in digital pathology struggle to accurately estimate physiologically plausible stain component images from tissue slides, especially when reference stain vectors are unknown or variable, leading to non-linear optimization problems and inconsistent results.
Innovation Solution
A tissue analysis system and method that estimates stain component images by adopting prior knowledge of stain variability, using a Gaussian prior distribution, and iteratively applying the Expectation-Maximization algorithm to deconvolute RGB images into H&E components, while accounting for heterogenous pixels through artificial latent variables.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional color deconvolution methods are used to estimate stain component images from RGB images, then the process is computationally straightforward, but the results are inconsistent and non-physiologically plausible when reference stain vectors are unknown or variable
Solution Approach 1:
The patent performs preliminary action by estimating reference stain vectors from training images before the actual deconvolution process. This pre-estimation step creates a foundation of known reference vectors that can be reused, avoiding the need to solve the ill-posed problem during each deconvolution operation. The training phase prepares the system in advance, storing reference vectors that will be used in the Expectation-Maximization algorithm during actual analysis.
Solution Approach 2:
The patent implements feedback through the Expectation-Maximization (EM) algorithm, which iteratively refines estimates of both reference stain vectors and stain component images. The EM algorithm alternates between estimating reference vectors from current component images (E-step) and updating component images using estimated references (M-step), creating a feedback loop that progressively improves solution plausibility and consistency.
2Ease of manufacture
If reference stain vectors are assumed to be fixed and known, then the deconvolution problem becomes linear and computationally simple, but this assumption fails when stain variability exists in practice
Solution Approach 1:
The patent applies dynamics by making the reference stain vectors adaptive rather than fixed. The system dynamically estimates reference vectors from training images and updates them through the EM algorithm based on the actual data being processed. This dynamic approach allows the reference vectors to adapt to stain variability while maintaining computational tractability through the iterative refinement process.
Solution Approach 2:
The patent changes parameters by transitioning from fixed reference vectors to variable, data-driven reference vectors. The system estimates reference stain vectors from training data and allows them to vary based on the specific tissue and staining conditions encountered. This parameter change enables the system to handle stain variability while maintaining a structured approach through the EM algorithm.
3Measurement precision
If the deconvolution algorithm accounts for stain variability through iterative methods, then measurement accuracy improves, but computational time and complexity increase
Solution Approach 1:
The patent performs preliminary action by pre-estimating reference stain vectors from training images before actual deconvolution. This pre-computation stores valuable information that accelerates subsequent processing, allowing the system to start with informed initial estimates rather than from scratch, thereby reducing iterative computation time while maintaining accuracy.
Solution Approach 2:
The patent applies partial action by separating the deconvolution process into two phases: a training phase that performs comprehensive iterative optimization to establish reference vectors, and an application phase that uses these pre-established references for faster processing. This partial completion of the full optimization in advance allows quicker subsequent analyses with reduced computational burden.
Data Source
AI summary
A tissue analysis system and method for the spectral deconvolution of a RGB digital image obtained from a stained biological tissue sample, by estimating the stain component images that are obtained from a staining system configuration, where the reference stain vectors are assumed to be sampled from a known color distribution. The prior knowledge of stain variability of the staining system is adopted as initial reference stain vectors and statistical distribution of their variability. Based on the initial reference stain vectors distribution, the tissue analysis system determines both the reference stain vectors and stain component images of the input image. The image is then deconvoluted based on the reference stain vectors and stain component images.