Immunofluorescence Pattern Detection via Two-Stage Neural Networks
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Solution Overview
Problem
Existing methods for detecting fluorescence patterns in immunofluorescence images of biological cell substrates are inefficient and computationally demanding, often requiring complex neural networks to analyze large images, leading to high computational effort and potential misclassification of irrelevant features.
Innovation Solution
A two-stage neural network approach is employed, where a first neural network identifies relevant subregions and determines localization information and partial confidence measures, followed by a second neural network analyzing each subregion separately to determine the presence of fluorescence patterns, allowing for more accurate and efficient classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a complex neural network is used to analyze the entire fluorescence image, then the detection accuracy may be improved, but the computational effort and processing time increase significantly
Solution Approach 1:
The patent divides the fluorescence image into multiple subregions and processes each subregion separately using a neural network. This segmentation approach reduces the computational complexity for each processing step while maintaining overall detection accuracy, directly resolving the contradiction between detection accuracy and processing time.
2Reliability
If the entire fluorescence image is processed, then all potential fluorescence patterns are captured, but the computational complexity and resource requirements increase
Solution Approach 1:
The image is segmented into subregions that are processed independently, reducing the computational burden on each processing unit while maintaining comprehensive coverage of the entire image for reliable pattern detection.
Solution Approach 2:
The patent extracts and processes only the relevant subregions containing potential fluorescence patterns, removing unnecessary computational processing of background areas, thus reducing overall system complexity while maintaining detection reliability.
3Productivity
If a simple processing method is used, then the processing speed increases, but the classification accuracy decreases due to misclassification of irrelevant features
Solution Approach 1:
By segmenting the image into manageable subregions, the system can apply efficient processing to each region while maintaining accurate classification through focused analysis, avoiding the misclassification issues that arise from processing entire large images with simple methods.
Solution Approach 2:
The patent applies different processing strategies to different subregions based on their characteristics, allowing for accurate classification in regions containing fluorescence patterns while maintaining high processing speed in background regions, thus resolving the contradiction between speed and accuracy.
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 reduces computational complexity and improves the validity of fluorescence pattern detection by focusing on relevant subregions, providing a reliable confidence measure through combined partial confidence measures from both stages.
Implementation Method 1
Such a fluorescent dye is preferably a green fluorescent dye, in particular the fluorescent dye FITC. Such binding of a primary antibody together with a fluorescently labeled secondary antibody can then be visualized later by irradiating the substrate with excitation light of a specific wavelength, thus exciting the bound fluorescent dyes to emit fluorescent radiation.
Data Source
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AI summary
A method is proposed for detecting the presence of a fluorescence pattern on an immunofluorescence image of a biological cell substrate, comprising the following steps: incubating the cell substrate with a liquid patient sample potentially containing primary antibodies, as well as with secondary antibodies labeled with a fluorescent dye; irradiating the cell substrate with excitation radiation and acquiring the immunofluorescence image; determining localization information indicating the respective locations of relevant sub-regions of the cell substrate in the fluorescence image; determining first-order confidence measures of the presence of the fluorescence pattern on the respective sub-regions using a first-order neural network based on the entire fluorescence image; and extracting the respective sub-image regions corresponding to the respective sub-regions of the cell substrate.from the fluorescence image based on the localization information, determining respective second partial confidence measures of the respective presences of the fluorescence pattern on the respective sub-areas using a second neural network based on the respective sub-image areas, determining a confidence measure of the presence of the fluorescence pattern in the fluorescence image based on the first partial confidence measures and the second partial confidence measures.