Crithidia Luciliae Fluorescence Imaging with CNN Binding Detection
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Solution Overview
Problem
Existing methods for detecting autoantibodies against double-stranded DNA (dsDNA) in patient samples using Crithidia luciliae cells and fluorescence microscopy are prone to errors due to the complexity of distinguishing kinetoplast regions from other cellular structures, leading to inaccurate binding measurements.
Innovation Solution
A method utilizing two pretrained convolutional neural networks (CNNs) for digital image processing: the first CNN identifies sub-images representing Crithidia luciliae cells, and the second CNN determines the extent of autoantibody binding specifically in the kinetoplast regions, eliminating the need for additional staining and reducing the complexity of image processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image processing methods are used to detect kinetoplast regions, then the process is simpler, but the accuracy of autoantibody binding measurement deteriorates due to difficulty in distinguishing kinetoplast regions from other cellular structures
Solution Approach 1:
The patent introduces convolutional neural networks as an intermediary between the fluorescence microscopy system and the final measurement. The CNN model acts as a mediator that automatically distinguishes kinetoplast regions from other cellular structures, resolving the contradiction by providing high measurement precision through learned features while keeping the operational process relatively simple through automated processing.
Solution Approach 2:
The patent replaces traditional manual or rule-based image processing methods with deep learning-based automated processing. This substitution transforms the measurement process from a complex manual procedure to an automated system that achieves higher precision in identifying kinetoplast regions and measuring autoantibody binding.
2Measurement precision
If additional staining steps are introduced to improve kinetoplast visibility, then the detection accuracy improves, but the process time and operational complexity increase
Solution Approach 1:
The patent extracts and isolates the specific task of kinetoplast region identification from the overall image processing workflow by using a specialized CNN model trained to recognize these regions. This extraction allows the system to achieve high detection accuracy using only the existing fluorescent stain, eliminating the need for additional staining steps and reducing processing time.
Solution Approach 2:
The patent performs preliminary training of the convolutional neural network model with labeled images of kinetoplast regions before actual measurement. This preliminary action enables the model to automatically distinguish kinetoplast regions from other cellular structures during measurement, achieving high detection accuracy without requiring additional staining or prolonged processing steps during the actual assay.
3Reliability
If manual identification of kinetoplast regions is performed, then the system complexity is lower, but the reliability and consistency of binding measurements deteriorates
Solution Approach 1:
The patent implements a self-service system where the convolutional neural network automatically identifies and measures kinetoplast regions without requiring manual intervention. The model serves itself by learning from training data and then autonomously performing region identification and binding measurement, ensuring high reliability and consistency while reducing human error and variability in the measurement process.
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 enhances the accuracy of autoantibody detection by precisely localizing kinetoplast regions and measuring binding, reducing errors and improving the reliability of dsDNA antibody detection.
Implementation Method 1
After irradiation of the incubated substrate with excitation light, a fluorescence radiation emitted by the green fluorescent dye can then be acquired as a fluorescence microscopy micrograph.
Data Source
AI summary
There is proposed a method for detecting a binding of autoantibodies from a patient sample to double-stranded deoxyribonucleic acid using Crithidia luciliae cells by means of fluorescence microscopy, including the steps of: provision of a substrate which has multiple Crithidia luciliae cells, incubation of the substrate with the patient sample which potentially has the autoantibodies, incubation of the substrate with secondary antibodies which have each been labelled with a preferably green fluorescent dye, acquisition of a fluorescence image of the substrate, identification by means of a first pretrained convolutional neural network of respective sub-images in the one fluorescence image that each represent a Crithidia luciliae cell, furthermore respective processing of at least one subset of the respective sub-images by means of a second pretrained convolutional neural network for determining respective binding measures which indicate a respective extent of a binding of autoantibodies in a respective kinetoplast region of a respective Crithidia luciliae cell of a respective sub-image, and determination of an overall binding measure with regard to a binding of autoantibodies from the patient sample to double-stranded deoxyribonucleic acid on the basis of the respective binding measures.