Stained Image Region Selection Using Cell-Density Color Mapping
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Solution Overview
Problem
In immunostaining analysis, selecting a suitable region for analysis from a tissue sample image is challenging due to the small number of stained cells and noise from tissue or cell structure, making it difficult for operators to visually recognize and select an appropriate region for analysis.
Innovation Solution
A region selection assistance method that segments a stained image into multiple regions, allocates display colors based on cell aggregation, generates a selection assistance image, and displays it on a screen to enhance visibility of stained cell regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If the entire tissue sample image is displayed at a magnification that shows the whole tissue, then the overall tissue structure is visible, but stained cells become difficult to visually recognize due to small size and noise interference
Solution Approach 1:
The image processing device divides the tissue sample image into multiple segmented regions and assigns different display colors to regions with different densities of stained cells. This segmentation allows the entire tissue area to be displayed while simultaneously highlighting regions containing stained cells through color differentiation, resolving the contradiction between showing the whole tissue and making stained cells visible.
Solution Approach 2:
The patent applies color changes by assigning different display colors to segmented regions based on the density of stained cells. Regions with higher density of stained cells are assigned colors that make them more conspicuous, while regions with lower density use different colors. This color-based differentiation enables visual recognition of stained cells throughout the entire tissue area without requiring magnification changes.
2Measurement precision
If the image is magnified to make stained cells visible, then stained cells can be recognized, but the overall tissue context is lost and noise from tissue structure remains problematic
Solution Approach 1:
The device segments the image into multiple regions and processes each region independently to calculate the density of stained cells. This segmentation approach enables precise detection of stained cells in each region while maintaining the overall tissue context in the composite displayed image, avoiding the need for extreme magnification that would lose contextual information.
Solution Approach 2:
The patent introduces an intermediary processing step that calculates the density of stained cells in each segmented region and uses this density information to assign appropriate display colors. This intermediary density calculation serves as a mediator between the raw image data and the final displayed image, enabling automatic identification of regions containing stained cells without manual intervention or complex filtering to remove tissue structure noise.
3Ease of operation
If manual visual inspection of the stained image is performed, then region selection can be made, but it requires significant time and expertise due to difficulty in recognizing stained cells
Solution Approach 1:
The image processing device performs self-service by automatically analyzing the stained image, segmenting it into regions, calculating stained cell density in each region, and generating a displayed image with color-coded regions. This automated process eliminates the need for manual visual inspection by operators, making region selection easier and significantly reducing the time required for analysis while maintaining accuracy in identifying regions containing stained cells.
Solution Approach 2:
The patent replaces the mechanical process of manual visual inspection with an automated image processing system. Instead of relying on operators to visually scan and identify regions with stained cells, the system uses computational image processing to automatically detect, segment, and highlight relevant regions through color coding, thereby substituting manual effort with automated computational analysis.
Data Source
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AI summary
A region selection assistance method includes a step of segmenting a stained image of a sample obtained by immunostaining into a plurality of segmented regions (step S11), a step of allocating one display color to each of the segmented regions from a display color group of a plurality of colors each having different conspicuity, to make each segmented region more conspicuous as the segmented region shows a higher aggregation of stained cells (step S12), a step of generating a selection assistance image in which each of the segmented regions is filled in with the display color allocated in step S12 (step S13), and a step of displaying the selection assistance image on a screen (step S14). This enables an operator to readily select a region of the stained image that is suitable for analysis as an analysis region.