Pathological Image Analysis for HER2 Grading Accuracy
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Solution Overview
Problem
Current methods for analyzing pathological sections, particularly for HER2 protein expression and gene amplification status in breast cancer, lack systematic cell membrane staining analysis, leading to inaccurate detection results.
Innovation Solution
A pathological section image processing method that involves obtaining stained images from multiple fields of view under a microscope, detecting cell nuclei, generating cell membrane description results, and determining cell types to provide a systematic analysis of cell membrane staining, thereby improving the accuracy of HER2 grading according to established diagnostic guidelines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If cell membrane staining analysis is performed on pathological sections, then the accuracy of HER2 detection is improved, but the complexity of the analysis system increases
Solution Approach 1:
The analysis system is divided into multiple independent modules: cell nucleus detection module, cell membrane staining analysis module, and HER2 grading module. Each module processes specific aspects of the pathological section independently, then integrates results to achieve accurate HER2 detection without requiring a fully complex integrated system.
Solution Approach 2:
The system performs preliminary cell nucleus detection and positioning before conducting cell membrane staining analysis. By pre-identifying cell nuclei locations, the system prepares the foundation for subsequent membrane analysis, reducing the complexity of simultaneous multi-task processing while maintaining high detection accuracy.
2Reliability
If systematic cell membrane staining analysis is implemented, then the reliability of HER2 grading is improved, but the time required for analysis increases
Solution Approach 1:
The analysis system processes pathological sections continuously through automated imaging and analysis pipelines. Multiple fields of view are captured and analyzed in sequence without interruption, maintaining continuous useful action that improves reliability through comprehensive coverage while minimizing total analysis time compared to间断性 (intermittent) processing.
Solution Approach 2:
The system performs self-verification through automated quality control checks and consistency validation during the analysis process. The algorithm automatically adjusts parameters and re-analyzes ambiguous cases without requiring manual intervention, ensuring high reliability while reducing time loss from human review cycles.
3Measurement precision
If multiple fields of view are analyzed for each pathological section, then the completeness of cell detection is improved, but the quantity of data processing increases
Solution Approach 1:
The system extracts only the essential features from each field of view (cell nucleus positions, membrane staining intensity, and key morphological characteristics) rather than processing complete high-resolution images. This extraction approach maintains detection completeness across multiple fields while significantly reducing the quantity of data that requires processing and storage.
Solution Approach 2:
Instead of processing all original high-resolution image data from multiple fields of view, the system creates simplified digital representations (copies) containing only the critical diagnostic information. These compressed data copies retain sufficient detail for accurate HER2 grading while occupying minimal storage space and processing bandwidth.
Data Source
AI summary
This application provides a pathological section image processing method performed by a computer device. The method includes: obtaining stained images of a pathological section after cell membrane staining; determining cell nucleus positions of cancer cells in a stained image under an ith field of view in the n fields of view; generating a cell membrane description result of the stained image under the ith field of view, the cell membrane description result being used for indicating completeness and staining intensity of the cell membrane staining; determining quantities of cells of types in the stained image under the ith field of view according to the cell nucleus positions and the cell membrane description result; and determining an analysis result of the pathological section according to quantities of the cells of types in the stained images under the n fields of view.