Fluorescence Image Analysis for Cell Type Determination
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Solution Overview
Problem
Existing image analysis methods for fluorescence images of cells fail to accurately determine the type of cell, as they either only assess the inclusion of the cell nucleus within the cell membrane or count cell nuclei without identifying cell types.
Innovation Solution
An image analysis apparatus and method that superimpose two fluorescence images, one stained for cell nuclei and another for specific cell membrane components, to determine if the cell nucleus is included within the cell membrane region, allowing for the differentiation of specific cell types from others based on staining overlap and contour analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If only cell nucleus inclusion is determined or cell nucleus count is performed, then the examination process is simple, but the cell type cannot be determined
Solution Approach 1:
The patent segments the image analysis into distinct processing channels: a first processing channel extracts cell nucleus information from a first fluorescence image, while a second processing channel extracts cell membrane information from a second fluorescence image. This segmentation allows complex multi-parameter analysis to be broken down into manageable components that can be processed independently and then integrated, resolving the contradiction between analysis precision and complexity.
Solution Approach 2:
The patent transitions from two-dimensional spatial analysis to three-dimensional analysis by extracting depth information from confocal microscopy images. The depth map generation and 3D coordinate calculation enable volumetric analysis of cell structures, providing additional dimensional data that improves cell type determination accuracy without proportionally increasing processing complexity.
2Productivity
If visual examination is used, then the equipment requirement is simple, but the evaluation efficiency is low and there is variability between observers
Solution Approach 1:
The image analysis apparatus performs automated extraction of cell nucleus contours, cell membrane contours, and depth information without requiring manual intervention. The system self-calibrates by automatically determining correspondence relationships between multiple images and performs quantitative analysis independently, eliminating observer variability and significantly improving examination efficiency while maintaining controlled complexity through algorithmic automation.
3Measurement precision
If multiple fluorescence images are acquired and processed, then the cell type determination accuracy is improved, but the processing time and computational load increase
Solution Approach 1:
The patent performs preliminary processing by extracting and storing cell nucleus contours from the first fluorescence image and cell membrane contours from the second fluorescence image before performing the actual cell type determination. Depth information is pre-calculated and stored as a depth map. These preliminary extractions are reused across multiple analysis operations, reducing redundant processing and minimizing overall processing time while maintaining high determination accuracy.
Solution Approach 2:
The patent replaces time-consuming manual contour tracing and measurement with automated image processing algorithms. Machine learning-based contour extraction and automatic feature measurement substitute for manual mechanical operations, dramatically reducing processing time while improving measurement precision and consistency.
Data Source
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AI summary
Provided are an image analysis apparatus, an image analysis method, and an image analysis program that can determine the type of cell included in an observation target. An image acquisition unit 21 acquires a first fluorescence image indicating an observation target including a plurality of types of cells, each of which has a first region stained by first staining, and a second fluorescence image indicating the observation target in which a second region of a specific cell among the plurality of types of cells is stained by second staining different from the first staining. A first determination unit 22 determines whether or not the first region is included in the second region in a superimposed image obtained by superimposing the first fluorescence image and the second fluorescence image and acquires a first determination result for each of the first regions. A second determination unit 23 determines the type of cell included in the observation target on the basis of the first determination result.