Fluorescence Image Analyzer Automates Cell Translocation Detection
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Solution Overview
Problem
Current methods for detecting abnormal cells using fluorescence in situ hybridization (FISH) are labor-intensive and rely heavily on operator interpretation, leading to variability and reduced accuracy in determining whether a sample is positive or negative for specific diseases, especially when analyzing large numbers of cells.
Innovation Solution
A fluorescence image analyzer that automates the analysis of cells by hybridizing nucleic acid probes labeled with fluorescent dyes to specific gene sites, such as the BCR and ABL genes, and uses image processing to extract and analyze bright spots, allowing for accurate determination of cell translocations without operator dependence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual observation and interpretation of cells by operator is used, then flexibility and adaptability in analysis are maintained, but accuracy and reliability deteriorate due to operator burden and dependence on operator senses
Solution Approach 1:
The patent replaces the manual mechanical observation and interpretation process with an automated image analysis system. The system captures fluorescent images of cells, processes them through algorithms to identify characteristic patterns, and automatically determines positive or negative samples, eliminating operator burden while maintaining or improving accuracy.
Solution Approach 2:
The analysis system performs self-service by automatically processing images and making determinations without requiring operator intervention. The system independently executes the full analysis workflow from image capture to result generation, reducing dependence on operator senses and capabilities.
2Measurement precision
If a huge number of cells (1,000 to 10,000 cells) are observed to accurately determine sample status, then accuracy of determination improves, but detection efficiency and productivity deteriorate
Solution Approach 1:
The system creates and analyzes multiple image copies or frames simultaneously, processing numerous cell images through automated algorithms. This allows efficient evaluation of many cells without the time burden of manual observation, maintaining accuracy while improving productivity through parallel processing capabilities.
Solution Approach 2:
The automated image analysis system replaces manual cell-by-cell observation with computer-based processing that can rapidly evaluate thousands of cells. The system uses algorithms to identify characteristic fluorescent patterns and make determinations much faster than human operators can manually examine cells.
3Productivity
If automated image analysis is implemented, then productivity and efficiency improve, but device complexity increases
Solution Approach 1:
The system integrates multiple functions into a single platform: image capture, image processing, pattern recognition, and result generation. By combining these functions in one universal system, the patent achieves high productivity while managing complexity through functional integration rather than separate components.
Solution Approach 2:
The patent introduces software algorithms and processing routines as intermediaries between the image capture device and the final determination. These intermediary processing layers automate the analysis workflow, improving productivity while containing complexity within the software domain rather than requiring complex hardware modifications.
4Measurement precision
If standard fluorescent image analysis methods are used, then ease of operation is maintained, but measurement precision deteriorates due to inability to detect subtle variations in bright spot characteristics
Solution Approach 1:
The system applies specialized analysis focused on local characteristics of bright spots within fluorescent images. By concentrating processing power and algorithmic attention on specific local features (brightness, size, shape, distribution patterns), the system achieves high measurement precision for subtle variations without requiring complex overall system changes.
Solution Approach 2:
The patent analyzes multiple parameters of bright spots simultaneously (brightness intensity, spot size, shape characteristics, spatial distribution) rather than relying on single-parameter visual assessment. This multi-parameter approach improves measurement precision by capturing subtle variations that single-parameter methods miss, while the automated processing maintains ease of operation.
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
The system significantly enhances the accuracy of determining whether a sample is positive or negative by automating the analysis of cell images, reducing operator variability and the need to examine large numbers of cells, thereby improving diagnostic reliability.
Implementation Method 1
the fluorescence generated due to the labeled probe is detected
Data Source
Figure 1
Figure 2(a)~2(d)
Figure 3(a)~3(d)
AI summary
A fluorescence image analyzer, analyzing method, and pretreatment evaluation method capable of determining with high accuracy whether a sample is positive or negative are provided. A pretreatment part 20 performs pretreatment including a step of labeling a target site with a fluorescent dye to prepare a sample 20a. A fluorescence image analyzer 10 measures and analyzes the sample 20a. The fluorescent image analyzer 10 includes light sources 121 to 124 to irradiate light on the sample 20a, imaging part 154 to capture the fluorescent light given off from the sample 20a irradiated by light, and processing part 11 for processing the fluorescence image captured by the imaging part 154. The processing part 11 extracts the bright spot of fluorescence generated from the fluorescent dye that labels the target site from the fluorescence image for each of a plurality of cells included in the sample 20a, and generates information used for determining whether the sample 20a is positive or negative based on the bright spots extracted for each of the plurality of cells.