Immunostaining Mask for Selective FISH Analysis
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Solution Overview
Problem
Conventional fluorescence in situ hybridization (FISH) analysis in carcinoma tumor samples is hindered by the inclusion of non-epithelial cells, leading to less accurate dot counting measurements due to the lack of effective methods to selectively analyze epithelial cells.
Innovation Solution
A computer-implemented method processes image data by using an immunofluorescent marker to classify cells as epithelial or non-epithelial, filtering FISH analysis results to exclude non-epithelial cells, and employing techniques like wavelet transforms and Voronoi partitions to refine the analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional FISH analysis measures FISH signal for all cells in a field of view, then the analysis process is simple and quick, but the measurement accuracy deteriorates because non-epithelial cells contribute to the dot counting statistics
Solution Approach 1:
The analysis process is segmented into distinct steps: first acquiring an immunofluorescence image to identify epithelial cells using cytokeratin markers, then acquiring a FISH image, and finally analyzing only the FISH signals from identified epithelial cells. This segmentation allows accurate cell-type-specific measurement while maintaining automated processing.
Solution Approach 2:
An immunofluorescence image acquisition step is introduced as an intermediary between sample preparation and FISH analysis. This intermediary step provides cell type identification information that guides the subsequent FISH signal analysis, enabling accurate filtering of epithelial cell signals from non-epithelial cell signals.
2Measurement precision
If manual intervention is used to select epithelial cells for FISH analysis, then the measurement accuracy improves, but the productivity and automation level deteriorate
Solution Approach 1:
The system performs self-service by automatically acquiring immunofluorescence images, identifying epithelial cells based on cytokeratin marker expression, and filtering FISH signals from only those epithelial cells. This automated self-service eliminates the need for manual cell selection while maintaining measurement accuracy.
Solution Approach 2:
The system changes the parameter of cell selection from manual visual inspection to automated fluorescence intensity thresholding. By setting intensity thresholds for cytokeratin marker detection, the system automatically identifies epithelial cells based on their fluorescent signal characteristics, enabling high-throughput automated analysis.
3Reliability
If no cell type classification is performed, then the ease of operation is maintained, but the reliability of FISH analysis results deteriorates due to inclusion of non-epithelial cells
Solution Approach 1:
Cell type classification is performed as a preliminary action before FISH signal analysis. The system first acquires and processes immunofluorescence images to classify cells as epithelial or non-epithelial based on cytokeratin marker expression, then uses this classification to guide the subsequent FISH analysis. This preliminary classification ensures reliable results while maintaining operational simplicity through automation.
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 approach enables accurate, automated FISH analysis by selectively focusing on epithelial cells, improving the precision of FISH dot counting statistics and enhancing the reliability of the results.
Implementation Method 1
receiving, by the processor, a first image of the tissue sample containing signals from an immunofluorescent (IF) morphological marker, wherein the tissue sample is stained with the IF morphological marker
Implementation Method 2
receiving, by the processor, a second image of the same tissue sample containing signals from a fluorescent probe, wherein the tissue sample is hybridized in situ with the fluorescent probe
Implementation Method 3
the act of segmenting the nuclei may include applying, by the processor, a wavelet transform to the second image
Data Source
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AI summary
A computer-implemented method of processing image data representing biological units in a tissue sample includes receiving a first image of the tissue sample containing signals from an immunofluorescent (IF) morphological marker, wherein the tissue sample is stained with the IF morphological marker, and receiving a second image of the same tissue sample containing signals from a fluorescent probe, wherein the tissue sample is hybridized in situ with the fluorescent probe. The method further includes classifying each biological unit in the tissue sample into one of at least two classes based on a mean intensity of the signals from the IF morphological marker in the first image, performing a fluorescence in situ hybridization (FISH) analysis of the tissue sample in the second image to obtain results therefrom, and filtering the results of the FISH analysis to produce a subset of the results pertaining to biological units classified in one class.