Immunofluorescence Pattern Detection Using Dual Neural Networks
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for analyzing immunofluorescence images require complex neural networks that perform a large number of computational operations, making them difficult to train and less reliable for detecting specific fluorescence patterns in biological cell substrates.
Innovation Solution
The method employs two neural networks: one for segmentation and one for confidence measurement, focusing on specific regions of the immunofluorescence image to detect fluorescence patterns, reducing the complexity and improving training efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single complex neural network is used to analyze the entire immunofluorescence image, then comprehensive pattern recognition is achieved, but the computational complexity increases and training becomes difficult
Solution Approach 1:
The patent divides the immunofluorescence image analysis into two distinct neural networks: a segmentation network that divides the image into relevant regions (e.g., epidermis, dermis, blister spaces) and a classification network that detects fluorescence patterns in those regions. This segmentation of the analytical task reduces the complexity of each individual network while maintaining comprehensive pattern recognition capability.
2Reliability
If the entire immunofluorescence image is analyzed, then all potential fluorescence patterns are detected, but the computational operations increase significantly
Solution Approach 1:
The segmentation network extracts only the relevant regions from the entire immunofluorescence image (such as epidermis, dermis, and blister spaces) and passes only these extracted regions to the classification network. This extraction approach maintains comprehensive pattern detection by focusing on all diagnostically relevant areas while significantly reducing the computational energy required compared to analyzing the entire image.
3Measurement precision
If a complex neural network is used, then comprehensive analysis is performed, but training efficiency decreases
Solution Approach 1:
By segmenting the analysis task into two specialized neural networks with distinct functions (segmentation and classification), each network can be trained more efficiently on its specific task. The segmentation network learns to identify anatomical regions, while the classification network learns to detect fluorescence patterns, thereby improving overall training efficiency while maintaining high detection precision.
4Area of stationary object
If the entire image is processed by one neural network, then complete coverage is achieved, but the system becomes less reliable for specific pattern detection
Solution Approach 1:
The segmentation network extracts all diagnostically relevant regions from the entire image (epidermis, dermis, blister spaces, etc.), ensuring complete coverage. These extracted regions are then passed to the classification network which specializes in detecting specific fluorescence patterns within those regions, thereby improving reliability for specific pattern detection while maintaining complete image coverage.
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 allows for reliable detection of fluorescence patterns in immunofluorescence images, providing accurate differentiation between bullous autoimmune dermatoses, such as bullous pemphigoid and epidermolysis bullosa acquisita, with high sensitivity and specificity.
Implementation Method 1
These secondary antibodies can then be visualized later by labeling the secondary antibodies with a fluorescent dye. 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
Figure 1
Figure 2a~2c
Figure 3
AI summary
A method is proposed for detecting at least one fluorescence pattern on an immunofluorescence image of a biological cell substrate, comprising the steps of: 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 segmentation information comprising at least a first and a second segmentation region, each representing a respective cell substrate region, by segmenting the immunofluorescence image using a first neural network; and determining a boundary region representing a transition from the first cell substrate region to the second cell substrate region in the fluorescence image, based on the segmentation information.Selecting multiple partial images from the immunofluorescence image along the boundary region, determining a confidence measure of the presence of the fluorescence pattern based on the multiple partial images using a second neural network.