IHC Image Analysis Using Nucleus Segmentation and Cell Scoring
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Solution Overview
Problem
Conventional immunohistochemistry analysis methods rely on subjective judgment, lacking an objective method to calculate scores for stained cells.
Innovation Solution
A computer-based method using machine learning and image processing techniques to segment cell nuclei, identify cytoplasmic pixels, and calculate pixel and cell staining scores, employing algorithms like watershed and morphological dilation to enhance objectivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional subjective judgment methods are used for IHC analysis, then the analysis process is simple, but the measurement precision and objectivity are poor
Solution Approach 1:
The patent replaces the manual subjective judgment mechanism with an automated computer-based image processing system. The system uses algorithms to segment cell nuclei, identify cytoplasmic regions, and calculate staining scores objectively, eliminating human subjectivity while maintaining analytical capability.
Solution Approach 2:
The system enables self-service automation where the computer automatically performs nucleus segmentation, cytoplasm identification, and staining score calculation without requiring manual intervention. The algorithm independently processes the entire analysis workflow from raw images to quantitative results.
2Productivity
If automated image processing algorithms are implemented, then the productivity and objectivity improve, but the device complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the complex analysis task into distinct modules: nucleus segmentation using machine learning models, cytoplasmic pixel identification through color space transformation, and staining score calculation. This modular approach manages complexity while maintaining high productivity.
Solution Approach 2:
The system transforms image data from RGB color space to HSV color space, changing the parameter representation to facilitate better cytoplasmic region identification. This parameter transformation enables more effective filtering and analysis of staining patterns.
3Measurement precision
If multiple processing steps are applied to identify cytoplasm, then the measurement precision improves, but the loss of time increases
Solution Approach 1:
The patent performs preliminary action by first segmenting cell nuclei using machine learning models before identifying cytoplasmic regions. This pre-processing step establishes reference points that accelerate subsequent cytoplasm identification, reducing overall processing time while maintaining precision.
Solution Approach 2:
The system applies local quality by using different processing strategies for different regions: machine learning models for nucleus detection and color space transformation with specific thresholds for cytoplasmic pixel identification. This region-specific approach optimizes both accuracy and efficiency.
Data Source
AI summary
A method for analyzing an immunohistochemistry (IHC) image is provided and includes: segmenting nuclei from the IHC image according to a machine learning model; removing pixels belonging to the nuclei and pixels in a color range from the IHC image to obtain multiple cytoplasmic pixels; assign the cytoplasmic pixels to the nuclei to form multiple cells according to the locations of the cytoplasmic pixels; and calculate a pixel staining score of each pixel in the cells, thereby calculating a cell staining score for each cell.