Immunofluorescence Pattern Detection via Organ-Layer Segmentation
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Solution Overview
Problem
Immunofluorescence microscopy methods face challenges in reliably detecting fluorescence patterns on organ segments due to incomplete coverage of relevant organ layers and inadequate image capture, leading to erroneous results.
Innovation Solution
A method utilizing a neural network for simultaneous segmentation and confidence measurement in fluorescence images, ensuring that relevant organ layers are adequately present and valid before determining the presence of fluorescence patterns, by transforming images into feature space and optimizing the neural network for both tasks concurrently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If digital image processing is used to detect fluorescence patterns on organ segments, then detection automation is improved, but reliability deteriorates due to incomplete coverage of relevant organ layers and inadequate image capture
Solution Approach 1:
The patent applies preliminary action by performing segmentation of the fluorescence image into organ layers before pattern detection. The neural network first segments the image to identify and separate different organ layers (such as mucosa, muscularis, submucosa), ensuring that relevant layers are properly identified and covered before the actual fluorescence pattern detection takes place. This preliminary segmentation step prevents erroneous results by ensuring adequate coverage of relevant organ layers.
2Measurement precision
If the area fraction of relevant organ layers is increased to ensure reliable pattern detection, then measurement precision is improved, but device complexity increases due to the need for multi-layer segmentation and validation
Solution Approach 1:
The patent merges multiple functions into a single neural network model. The same neural network performs both segmentation of organ layers and detection of fluorescence patterns, rather than using separate processing systems for each task. This combining of functions maintains measurement precision by ensuring relevant organ layers are properly identified, while reducing device complexity by using one integrated neural network model instead of multiple separate processing systems.
3Device complexity
If conventional image processing methods are used without organ layer segmentation, then device complexity is reduced, but measurement precision deteriorates due to inability to ensure adequate coverage of relevant organ layers
Solution Approach 1:
The patent introduces segmentation information as an intermediary between the raw fluorescence image and the final pattern detection result. The neural network first generates segmentation information that identifies different organ layers, and this segmentation information is then used as a basis for accurate fluorescence pattern detection. This intermediary step ensures that the detection process focuses on the correct organ layers, thereby improving measurement precision without requiring overly complex external processing systems.
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
Enhances the reliability of fluorescence pattern detection by ensuring accurate coverage and validity of organ layers, reducing false positives and negatives, with an analytical sensitivity of 0.90 and specificity of 0.96.
Implementation Method 1
such secondary antibodies having been labelled with a fluorescent dye. Such a fluorescent dye is preferably a green fluorescent dye, especially the fluorescent dye FITC. Such binding of a primary antibody together with a fluorescently labelled secondary antibody can then be visualized later by irradiating the organ segment with excitation light of a particular wavelength and thus exciting the bound fluorescent dyes to emit fluorescence radiation.
Data Source
AI summary
There is proposed a method for detecting a presence of a fluorescence pattern type on an organ segment via immunofluorescence microscopy and digital image processing. The steps comprise: provision of the organ segment, incubation of the organ segment with a liquid patient sample, incubation of the organ segment with secondary antibodies which have been labelled with a fluorescent dye, acquisition of a fluorescence image of the organ segment in a colour channel corresponding to the fluorescent dye, and provision of the fluorescence image to a neural network. What takes place by means of the neural network is simultaneous determination of segmentation information through segmentation of the fluorescence image and, furthermore, of a measure of confidence indicating an actual presence of the fluorescence pattern type. What further takes place is determination, on the basis of the previously determined segmentation information, of at least one sub-area of the fluorescence image that is relevant to formation of the fluorescence pattern type, determination, on the basis of the previously determined at least one sub-area, of validity information indicating a degree of a validity of the measure of confidence, and output of the measure of confidence depending on the validity information.