Dot Detection and Color Classification in Tissue Specimens
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Solution Overview
Problem
Current methods for detecting and classifying dot pixels in tissue samples, particularly in dual ISH for HER2 detection, face challenges such as image variations, incorrect classification of faint dots, and failure to detect or classify dots due to factors like image background, optical fringing, and poor focus, leading to inaccuracies and inconsistencies in gene expression analysis.
Innovation Solution
A computing device configured to detect and classify dot pixels in tissue samples using criteria such as absorbance strength, difference of Gaussian threshold, and color channel thresholds, with refinement techniques like morphological operations and radial symmetry voting to improve accuracy and reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated image analysis is used to detect and classify dots in tissue samples, then productivity and speed of gene expression analysis are improved, but measurement precision and reliability deteriorate due to image variations, background interference, and incorrect classification of faint dots
Solution Approach 1:
The image processing is divided into multiple sequential stages: initial dot detection, classification into color categories, refinement of detected dots, and final counting. Each stage processes specific aspects of the image data independently, allowing optimization for both speed and accuracy at different processing levels.
Solution Approach 2:
The refinement process uses feedback from the initial classification to improve subsequent detection. Dots that were initially detected are re-evaluated using refined criteria that incorporate information from the classification stage, allowing correction of false positives and false negatives while maintaining overall processing efficiency.
2Speed
If automated image analysis with simple detection criteria is used, then processing speed is improved, but reliability deteriorates due to false negatives and incorrect classification of faint dots
Solution Approach 1:
The system performs preliminary dot detection using simplified criteria to identify candidate dots quickly, then applies more sophisticated refinement processes only to these candidates. This preliminary action separates the fast initial screening from the slower but more accurate refinement stage.
Solution Approach 2:
The detection and classification criteria are dynamically adjusted based on image characteristics. Thresholds for dot detection and color classification are optimized for each specific image to balance speed and reliability, allowing the system to adapt to varying image qualities and conditions.
3Measurement precision
If multiple detection criteria and refinement techniques are applied, then measurement precision and reliability are improved, but device complexity and computational requirements increase
Solution Approach 1:
The complex image processing task is segmented into distinct functional modules: detection module, classification module, refinement module, and counting module. Each module handles a specific aspect of the analysis, making the overall complex system manageable and allowing independent optimization of each component.
Solution Approach 2:
The classification stage acts as an intermediary between initial detection and final refinement. It provides structured information about detected dots that guides the refinement process, reducing the complexity of the refinement stage by pre-organizing the data according to color categories and confidence levels.
4Productivity
If automated analysis is used to overcome manual interpretation limitations, then productivity is improved, but measurement precision deteriorates due to difficulties in detecting faint dots and handling image variations
Solution Approach 1:
The detection thresholds and classification criteria are made dynamic rather than fixed. The system adapts its parameters based on the specific characteristics of each image, including the overall brightness, contrast, and distribution of signal intensities, allowing it to effectively detect faint dots across varying image conditions.
Solution Approach 2:
Multiple detection parameters are adjusted simultaneously to optimize faint dot detection: absorption thresholds, color channel weights, and refinement criteria are all modified based on image-specific characteristics. This coordinated parameter adjustment maintains high sensitivity for faint signals while preserving overall processing efficiency.
Data Source
AI summary
The present disclosures relates to a method of detecting, classifying, and counting dots in an image of a tissue specimen comprising detecting dots in an image of the tissue sample that meet criteria for absorbance strength, black unmixed image channel strength, red unmixed image channel strength, and a difference of Gaussian threshold, wherein the detected dots correspond to in situ hybridization signals in the tissue samples; classifying the detected dots as belonging to a black in situ hybridization signal or to a red in situ hybridization signal; and calculating a ratio of those dots belonging to the black in situ hybridization signal and those belonging to the red in situ hybridization signal.