Automated HER2 Scoring via Optical Transformations
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Solution Overview
Problem
The current method for HER2 scoring in breast cancer tissue samples relies on visual analysis by pathologists, which is time-consuming and results in inconsistent outcomes, necessitating an efficient and automated approach.
Innovation Solution
An apparatus and method for automatic HER2 scoring that involves selecting salient regions in tissue samples, calculating stain vectors, performing optical domain transformations, identifying nuclei and membrane patterns, removing non-important areas, and classifying cells to compute a final score based on cell classifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If visual analysis by pathologist is used for HER2 scoring, then diagnostic accuracy can be maintained, but the process is time-consuming and results are inconsistent
Solution Approach 1:
The patent replaces the manual visual analysis mechanism with an automated image processing system that uses optical domain transformations and algorithmic analysis to determine HER2 scores, thereby eliminating time consumption and inconsistency associated with human pathologists while maintaining diagnostic accuracy
Solution Approach 2:
The system creates a digital copy of the tissue sample image and performs multiple optical transformations (HIST, RGB, HSV, CIELAB) on this copy to extract HER2 scoring information, allowing repeated analysis without consuming the original sample and enabling consistent automated measurement
2Productivity
If automated image processing is implemented, then scoring speed and consistency are improved, but system complexity increases
Solution Approach 1:
The patent segments the complex image processing task into distinct stages: optical domain transformation, color space conversion, membrane detection, and scoring calculation. Each stage handles a specific aspect of the analysis, making the overall complex system manageable and maintainable while achieving high productivity
Solution Approach 2:
The system transforms images through multiple parameter spaces (HIST to RGB to HSV to CIELAB), optimizing the representation at each stage to facilitate specific analysis tasks. This parameter transformation approach enables efficient automated processing despite the underlying computational complexity
3Measurement precision
If multiple optical domain transformations are applied, then measurement precision is improved, but computational complexity increases
Solution Approach 1:
The patent applies different optical transformations and analysis methods to different regions and aspects of the image data. For example, specific color space conversions are applied to enhance particular features needed for membrane detection, while other transformations focus on nuclear identification, optimizing precision for each local analysis task
Data Source
AI summary
Certain aspects of an apparatus and method for method and apparatus for automatic HER2 scoring of tissue samples may include for determining a cancer diagnosis score comprising identifying one or more nuclei in a slide image of a tissue sample, determine one or more membrane strengths in the slide image surrounding each of the one or more nuclei, classifying one or more cells, each corresponding to the one or more nuclei, in a class among a plurality of classes according to the one or more membrane strengths and determining a cancer diagnosis score based on a percentage of cells classified in each of the plurality of classes.