Immunofluorescence Pattern Detection with Segmented Organ Section Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing immunofluorescence microscopy methods face challenges in accurately detecting fluorescence patterns on organ sections due to incomplete coverage of the slide by biological material and variations in optical magnification, leading to unreliable diagnostic decisions.
Innovation Solution
A method utilizing two neural networks for segmentation and classification, where the first neural network segments the relevant sub-area of the fluorescence image, and the second determines the measure of confidence and validity information for pattern detection, providing robustness against incomplete coverage and optical variations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If the entire fluorescence image is analyzed by a single neural network, then the device complexity is reduced, but the reliability of pattern detection deteriorates due to incomplete organ section coverage and optical variations
Solution Approach 1:
The patent divides the image analysis task into two separate neural networks: a segmentation network that identifies the organ section sub-area, and a classification network that detects fluorescence patterns. This segmentation of functions resolves the contradiction by improving reliability through specialized processing while managing complexity through modular architecture.
Solution Approach 2:
The segmentation network acts as an intermediary between the raw fluorescence image and the pattern classification process. It generates a segmented sub-area that serves as input to the classification network, enabling reliable pattern detection by first isolating the relevant organ section region from the entire image.
2Loss of substance
If the organ section covers only a sub-area of the slide, then the loss of substance is reduced, but the measurement precision deteriorates due to incomplete coverage
Solution Approach 1:
The segmentation network extracts the specific sub-area containing the organ section from the entire fluorescence image. This extraction enables precise pattern measurement by focusing analysis only on the relevant region, compensating for the incomplete coverage and maintaining measurement precision despite using minimal organ material.
Solution Approach 2:
The patent applies local quality by performing pattern classification specifically on the segmented sub-area where the organ section is located, rather than uniformly processing the entire image. This localized approach maintains measurement precision by concentrating analysis resources on the actual sample region.
3Adaptability or versatility
If optical magnification varies across the image, then the adaptability of the system is improved, but the manufacturing precision deteriorates due to inconsistent pattern appearance
Solution Approach 1:
The neural networks are trained to dynamically adapt to varying optical magnifications and image conditions. The segmentation network dynamically identifies the organ section boundaries regardless of magnification, and the classification network dynamically classifies patterns based on learned features that are invariant to optical variations, maintaining consistency despite adaptability.
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 method enhances the reliability of fluorescence pattern detection by outputting both confidence measures and validity information, ensuring accurate diagnostic decisions even with partial organ section coverage and varying optical conditions.
Implementation Method 1
Such binding of a primary antibody together with a fluorescently labelled secondary antibody can then be visualized later by irradiating the organ section with excitation light of a certain wavelength and thus exciting the bound fluorescent dyes to emit fluorescence radiation
Data Source
AI summary
A method for detecting a potential presence of a fluorescence pattern type on an organ section via immunofluorescence microscopy and digital image processing, the method comprising: providing an organ section on a slide, incubating the section with a liquid patient sample potentially including primary antibodies and secondary antibodies labelled with a fluorescent dye, acquiring a fluorescence image of the organ section, determining, by segmentation of the fluorescence image via a first neural network, a sub-area of the fluorescence image relevant to formation of the fluorescence pattern type, determining, via a second neural network, the measure of confidence indicating an actual presence of the fluorescence pattern type, determining, validity information indicating a degree of a validity of the measure of confidence, and outputting the measure of confidence of the actual presence of the fluorescence pattern type and of the validity information.