Fluorescence Image Analysis for Chromosomal Abnormality Detection
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Solution Overview
Problem
Current fluorescence image analysis methods, such as the FISH method, face challenges in accurately determining the ratio of positive cells with chromosomal abnormalities, especially when the orientation of cells in the flow cell affects image analysis, leading to potential misclassification of negative cells as positive, which complicates disease diagnosis, particularly when the positive cell ratio is low.
Innovation Solution
A fluorescence image analysis method that involves labeling target sites on chromosomes with fluorescent dye, capturing images of cells, selecting test cells based on morphological characteristics, extracting bright spots from fluorescence images, and identifying cells with or without chromosomal abnormalities to generate accurate ratios of abnormal to normal cells, using an imaging unit and processing unit to analyze and classify cells.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a conventional FISH method with manual examiner observation is used, then the number of cells examined is limited to 100-200 per sample, but the accuracy of determining positive cell ratio is maintained through expert judgment
Solution Approach 1:
The patent replaces manual examiner observation with an automated image analysis system that captures fluorescence images and processes them through software algorithms. The system automatically detects bright spots, determines cell positivity, and calculates positive cell ratios, eliminating the need for manual microscopic examination while examining significantly more cells (tens of thousands) per sample.
Solution Approach 2:
The patent creates digital copies of cellular structures through fluorescence imaging. By capturing optical images of cells with fluorescently labeled probes, the system generates replicable digital representations that can be analyzed computationally, allowing multiple cells to be examined simultaneously with consistent criteria applied across all samples.
2Productivity
If automated fluorescence image analysis is used to increase the number of detected cells, then productivity increases by tens of thousands of times, but measurement precision deteriorates due to misclassification of negative cells as positive
Solution Approach 1:
The patent segments the image analysis process into distinct computational steps: cell region detection, bright spot extraction, brightness threshold determination, and positivity determination. By dividing the analysis into discrete stages with specific criteria for each, the system maintains measurement precision while processing large numbers of cells automatically.
Solution Approach 2:
The patent dynamically adjusts analysis parameters such as brightness thresholds based on the specific characteristics of each cell image. Rather than applying fixed criteria to all cells, the system adapts parameters to account for variations in cell morphology, fluorescence intensity, and imaging conditions, thereby maintaining accuracy across diverse cell populations.
3Measurement precision
If cell isolation procedures are implemented to improve diagnostic accuracy for low positive cell ratios, then measurement precision improves, but device complexity and ease of operation worsen due to cumbersome isolation workflows
Solution Approach 1:
The patent extracts only the essential diagnostic information (fluorescence signal from chromosomal abnormalities) directly from the original cell population without requiring physical isolation of specific cell types. By using fluorescent probes that specifically bind to abnormal chromosomal regions, the system identifies positive cells within the whole sample, eliminating the need for complex isolation procedures while maintaining diagnostic accuracy.
4Reliability
If fluorescent probes are used to label chromosomal target sites, then specificity of detection is improved, but device complexity increases due to the need for fluorescence imaging and image processing systems
Solution Approach 1:
The patent utilizes fluorescent dyes that emit light at specific wavelengths when excited, creating distinct color signals for different chromosomal targets. By employing fluorophores with different emission spectra, the system can simultaneously detect multiple chromosomal abnormalities using different colored probes, achieving high specificity while using a unified imaging platform that captures multiple wavelengths.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This method significantly improves the accuracy of diagnosing chromosomal abnormalities by efficiently distinguishing between positive and negative cells, even at low positive cell ratios, thereby facilitating more reliable disease diagnosis without the need for complex cell isolation processes.
Implementation Method 1
labeling a target site on a chromosome with fluorescent dye in a plurality of cells contained in a sample
Data Source
AI summary
A fluorescence image analyzer has an imaging unit for capturing a first image containing at least a part of a region of a cell as an imaging target for a plurality of cells in a sample in which a target site on a chromosome is labeled with a fluorescent dye, and a second image including fluorescence generated from a fluorescent dye labeling the target site of the cell of the first image. The processing unit selects a plurality of test cells having specific morphological characteristics to be tested from a plurality of cells based on at least the first image, and extracts the bright spots of fluorescence generated from the fluorescent dye. The processing unit identifies cells with chromosomal abnormalities and/or cells without chromosomal abnormalities based on the extracted bright spots, and generates information related to the ratio of cells with chromosomal abnormalities relative to the test cells.