Medical Image Processor for HER2 Protein Quantification
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Solution Overview
Problem
Current methods for detecting HER2 protein overexpression in cancer tissues, such as the DAB staining method, lack quantitative accuracy and rely heavily on user-dependent and labor-intensive processes, leading to potential oversight of tumor regions.
Innovation Solution
A medical image processor that analyzes both cell shape and fluorescent images to extract cell nuclei, identify fluorescent bright points, and calculate feature amounts for each cell region, determining cancer status and protein expression levels with reduced human intervention and increased accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If the DAB staining method is used to detect HER2 protein, then the process is simpler compared to FISH method, but the quantitative accuracy and measurement precision are insufficient
Solution Approach 1:
The patent introduces an image processing system as an intermediary between the DAB staining process and the final diagnosis. The system captures images of the stained tissue, extracts cell nuclei regions, identifies DAB-stained areas, and quantifies the HER2 protein expression automatically. This intermediary processing step transforms the simple but imprecise DAB staining into a quantitatively accurate measurement without complicating the staining procedure itself.
2Ease of operation
If manual evaluation of HER2 expression is performed by pathologists, then the method is easier to implement, but the reliability and consistency of results are low due to human dependency
Solution Approach 1:
The patent implements a self-service system where the image processing apparatus automatically performs all evaluation tasks without human intervention. The system independently captures images, processes them through multiple algorithms (nucleus extraction, DAB region identification, quantification), and generates diagnostic results. This eliminates human dependency while maintaining ease of operation, as the automated system handles the complex processing that would otherwise require skilled pathologists.
Solution Approach 2:
The system incorporates feedback mechanisms where the image processing results are continuously refined through iterative analysis. The apparatus evaluates multiple features (nucleus position, DAB staining intensity, cellular morphology) and uses this feedback to improve the accuracy of HER2 expression assessment. This automated feedback loop ensures consistent and reliable results across different samples and operators.
3Reliability
If the entire tissue slice is examined manually to identify all tumor regions, then comprehensive coverage is achieved, but the time consumption and labor intensity increase significantly
Solution Approach 1:
The patent replaces the manual mechanical examination process with an automated optical-mechanical system. The image processing apparatus uses automated image capture and digital processing to examine the entire tissue slice rapidly. The system substitutes the slow, labor-intensive manual scanning with fast automated imaging and algorithm-based analysis, achieving comprehensive coverage of the entire tissue area in a fraction of the time required for manual examination.
Solution Approach 2:
The system performs preliminary automated processing of the entire tissue slice to identify potential tumor regions before detailed analysis. By pre-processing the complete image set and flagging areas of interest, the system ensures comprehensive coverage is achieved quickly, allowing focused examination of only the relevant regions while maintaining complete oversight of the entire tissue sample.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables efficient identification of cancer regions with HER2 protein overexpression from entire tissue slices, improving diagnostic accuracy and reducing the risk of overlooking tumor regions.
Implementation Method 1
a fluorescent substance included nanoparticle which has a fluorescent substance included therein and bonds with a specific biological substance recognition site
Data Source
AI summary
A medical image processor and a storage medium are shown. According to one implementation, the medical image processor includes the following. An input unit is used to input a cell shape image and a fluorescent image showing expression of a specific protein. A cell nucleus extracting unit extracts a cell nucleus. A fluorescent bright point extracting unit extracts a fluorescent bright point. A region estimating unit sets a predetermined region. When the set region does not overlap with another, it is estimated to include one cell. When a plurality of the set regions overlap, it is estimated to include a plurality of cells. A feature amount calculating unit calculates a feature amount. A determining unit determines whether each estimated cell region is cancer and determines an expression status in the region based on the calculated feature amount. An output unit outputs a determination result.