Specimen Image Processing Using Inner-Region Cell Separation
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Solution Overview
Problem
Conventional methods struggle to accurately separate and analyze individual cells in high-density tissue specimens, particularly in multiplexed immunohistochemistry, due to overlapping cells in both the plane and depth directions, and are limited by specimen damage from excessive staining.
Innovation Solution
An image processing technique that extracts inner regions within cell membranes, isolates these regions, and expands them to define individual cells, allowing for precise cell discrimination and quantitative analysis even in high-density specimens.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the tissue section thickness is increased to 4 μm or more for multiplexed immunohistochemistry, then the number of staining times can be increased, but individual cells cannot be separated in the image due to overlapping in the depth direction
Solution Approach 1:
The patent applies focus gradient information across multiple depth planes (z-stack imaging) to resolve cell overlaps. By capturing images at multiple focal depths and analyzing the focus gradient, the system can distinguish overlapping cells in the depth dimension, enabling accurate cell separation even in thick tissue sections (4 μm or more) that support multiple staining cycles.
2Measurement precision
If the tissue section is thinned to improve cell discrimination, then individual cells can be separated more easily, but the specimen is easily damaged and the number of staining times is limited
Solution Approach 1:
Instead of thinning the specimen, the patent utilizes the depth dimension through z-stack imaging and focus gradient analysis. This approach maintains thick tissue sections (preserving specimen integrity and enabling multiple staining cycles) while achieving accurate cell discrimination by analyzing focal plane information across multiple depths.
Solution Approach 2:
The patent changes the parameter being measured from physical section thickness to optical focus gradient. By analyzing the gradient of focus across multiple depth planes, the system achieves accurate cell boundary detection without physically thinning the specimen, thus maintaining both cell discrimination accuracy and specimen integrity.
3Ease of operation
If conventional image processing is used to specify cell regions by expanding the nucleus by a predetermined size, then the process is simple, but accurate cell region specification is difficult in high-density specimens where cells are in contact
Solution Approach 1:
The patent moves from 2D nucleus-based expansion to 3D cell membrane tracking using focus gradient information. By analyzing the gradient of focus across multiple depth planes, the system can accurately trace cell membranes even in high-density specimens where cells are in contact, achieving precise cell region specification without simple predetermined expansion.
Solution Approach 2:
The patent replaces the mechanical/geometric approach of fixed-size nucleus expansion with an optical physics-based approach using focus gradient analysis. By utilizing the optical properties of light focus and defocus across multiple planes, the system achieves accurate cell boundary detection that adapts to varying cell densities and contact configurations.
Data Source
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AI summary
An image processing using a plurality of specimen images obtained by successively applying a plurality of types of staining to a specimen to be evaluated and imaging the specimen after staining for at least two types of staining is performed. The image processing comprises: extracting an inner region corresponding to inward of a cell membrane of a single cell in the specimen based on at least one of the specimen images; specifying a cell region corresponding to an individual cell included in the specimen by expanding the inner region outwardly; and performing image cytometry for the cell region based on the specimen images. In analyzing a pathological specimen on a cell-by-cell basis, it is possible to deal with multiple immunostaining and specify the positions of individual cells and evaluate each cell separately even when a cell density in the specimen is high.