Automatic ER/PR Scoring via Stain Vector Analysis
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Solution Overview
Problem
Current automatic scoring techniques for estrogen receptor (ER) and progesterone receptor (PR) in cancer diagnosis are inconsistent and time-consuming due to improper identification of stained cancer cells, relying on manual visual analysis by pathologists.
Innovation Solution
An apparatus and method for automatic ER/PR scoring of tissue samples using salient region selection, stain vector calculation, optical domain transformations, and pixel analysis to differentiate and count positive and negative stained nuclei, calculating a proportion score and intensity score for accurate ER/PR scoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual visual analysis by pathologists is used for ER/PR scoring, then diagnostic accuracy can be maintained, but time consumption increases and human error occurs
Solution Approach 1:
The patent replaces the manual mechanical visual analysis system with an automated digital image processing system. The system uses computer algorithms to analyze stained tissue samples, automatically identifying and scoring ER/PR positive cells without human intervention, thereby eliminating time consumption and human error while maintaining diagnostic accuracy.
Solution Approach 2:
The patent implements a self-service automated scoring system where the digital pathology system performs ER/PR scoring independently without requiring pathologist intervention. The system automatically processes images, identifies stained cells, calculates scores, and generates reports, making the scoring process self-sufficient and eliminating dependency on manual analysis.
2Loss of time
If current automatic scoring techniques are used, then time consumption is reduced, but identification accuracy of stained cancer cells deteriorates
Solution Approach 1:
The patent employs advanced parameter changes in the image processing algorithm, including multi-threshold segmentation, color space transformations, and adaptive filtering parameters. These parameter optimizations enable the system to accurately distinguish ER/PR positive cells from negative cells and background tissue, achieving high identification accuracy that matches or exceeds manual pathologist analysis.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously refines its cell identification and scoring based on image quality assessment and score validation. The algorithm adjusts processing parameters based on feedback from previous analysis results, improving identification accuracy iteratively and ensuring consistent performance across different tissue samples.
Data Source
AI summary
Certain aspects of an apparatus and method for automatic ER/PR scoring of tissue samples may include for determining a cancer diagnosis score comprising identifying a positive stained nucleus in a slide image of the tissue sample, identifying a negative stained nucleus in the slide image, computing a proportion score based on number of the positive stained nucleus identified and number of the negative stained nucleus identified and determining the cancer diagnosis score based on the proportion.