Pathology Image Segmentation for Pixel-Level Immune Cell Scoring
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Solution Overview
Problem
Existing methods for determining the immune cell score in pathological images rely on manual interpretation, resulting in low accuracy and lack of precise pixel-based results, with only two interpretation categories (greater than or equal to 1% or less than 1%).
Innovation Solution
An artificial intelligence-based pathological image processing method that involves determining a seed pixel corresponding to an immune cell region, obtaining a seed pixel mask image, segmenting an epithelial cell region, fusing the mask images to obtain an effective seed pixel mask image, and calculating the immune cell region's ratio value.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual interpretation methods are used to determine immune cell score, then the process is simple to operate, but the measurement precision and accuracy are low
Solution Approach 1:
The patent segments the pathological image processing into distinct functional modules: seed pixel identification module, mask image generation module, epithelial cell segmentation module, fusion module, and ratio calculation module. Each module handles a specific aspect of the analysis, enabling precise immune cell ratio measurement through systematic breakdown of the complex task.
Solution Approach 2:
The patent introduces mask images as intermediary representations between the original pathological image and the final ratio calculation. Seed pixel mask images and epithelial cell mask images serve as intermediate data structures that facilitate precise identification and differentiation of immune cells from other tissue elements.
2Measurement precision
If manual interpretation is used, then the method is easy to implement, but it only provides binary categorization (greater than or equal to 1% or less than 1%) without precise pixel-based results
Solution Approach 1:
The patent transitions from binary categorization (one-dimensional classification) to pixel-level quantitative analysis (two-dimensional precision measurement). By generating mask images at pixel level and calculating precise ratio values, the system provides detailed spatial information and exact measurements rather than simple categorical labels.
Solution Approach 2:
The patent changes the output parameter from binary categories to continuous ratio values with pixel-level precision. The system calculates exact percentages of immune cells in the pathological image, transforming the measurement from discrete classification to continuous quantitative parameter.
3Productivity
If automated AI-based processing is implemented, then the productivity and accuracy improve, but the device complexity increases
Solution Approach 1:
The patent creates a multi-functional processing system where the same AI framework performs multiple tasks: seed pixel identification, mask image generation, epithelial cell segmentation, and ratio calculation. This universal approach improves productivity by automating all steps while managing complexity through integrated design.
Solution Approach 2:
The patent performs preliminary actions by pre-processing the pathological image to identify seed pixels and generate mask images before the main ratio calculation. This preliminary segmentation and identification work prepares the data structure for efficient subsequent processing, improving overall productivity.
Data Source
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Figure 2
Figure 3~4A
AI summary
An artificial intelligence-based pathological image processing method and apparatus, an electronic device, and a computer-readable storage medium. The method comprises: determining, from a pathological image, a seed pixel corresponding to an immune cell region (101); acquiring, from the pathological image, and on the basis of the seed pixel corresponding to the immune cell region, a seed pixel mask image corresponding to the seed pixel (102); segmenting an epithelial cell region in the pathological image, and obtaining an epithelial cell mask image of the pathological image (103); performing fusion processing on the seed pixel mask image and the epithelial cell mask image of the pathological image, and obtaining a valid seed pixel mask image corresponding to the immune cell region in the pathological image (104); and determining, on the basis of the valid seed pixel mask image, the proportion of the pathological image occupied by the immune cell region (105).