Deep Neural Network for ELISPOT Spot Detection
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Solution Overview
Problem
Existing immune monitoring tools face challenges in distinguishing true signals from background noise in live cell-based assays, particularly due to varying spot sizes and clusters in ELISPOT and FLUOROSPOT assays, which complicates accurate data analysis and consistency across different instruments and laboratory techniques.
Innovation Solution
A non-parametric, user-independent deep machine learning-based method is employed for data analysis, utilizing a deep neural network trained with image data from multiple imaging devices to detect objects of interest, such as cell clusters or secreted products, ensuring consistent and accurate results across various machines and users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional parametric image analysis algorithms are used to count spots in ELISPOT/FLUOROSPOT assays, then the analysis can be automated, but the distinction between true signal spots and background noise becomes unreliable due to varying spot sizes and intensities
Solution Approach 1:
The patent transforms the spot detection problem from traditional intensity-based parametric analysis to deep learning-based image feature analysis. The system processes raw images through a trained neural network that automatically learns optimal features for distinguishing spots from background noise, eliminating the need for manual parameter adjustment and improving measurement precision while maintaining automation.
2Adaptability or versatility
If conventional analysis software is used to accommodate variations between different instruments and laboratory techniques, then user flexibility is maintained, but the results lack consistency and objectivity across different users and machines
Solution Approach 1:
The patent implements a universal deep learning model that can process images from different instruments and assay conditions without requiring user-specific parameter adjustments. The trained neural network learns invariant features that generalize across multiple devices and experimental conditions, providing consistent and objective results while maintaining the ability to handle various assay types through the same core algorithm.
3Measurement precision
If manual parameter adjustment is performed to optimize spot detection for specific instruments, then detection accuracy improves for that instrument, but the system becomes complex and requires ongoing maintenance and reconfiguration
Solution Approach 1:
The patent enables the system to automatically adapt to different instruments through self-service mechanisms. The deep learning model is pre-trained on diverse datasets representing multiple instruments and conditions, allowing it to autonomously optimize detection performance without requiring manual parameter tuning or user expertise. The system self-adjusts to new instruments through the inherent robustness of the learned features.
Data Source
AI summary
A method, apparatus and computer program product to provide machine learning-assisted detection of objects of interest in live cell-based assays, where the objects of interest are larger than cells used in the assay. The technique herein comprises receiving image data that has been captured from a set of imaging devices, such as immune monitoring analyzer machines. Representative image data is an enzyme-linked immune absorbent spot (ELISPOT) assay captured from an ELISPOT analyzer. For each set of image data, the image data is then processed using, for example, one of: (a) a first pre-trained model; and (b) a set of one or more detection algorithms, to generate training data comprising a set of labels for the image data. A model, e.g., a deep neural network (DNN), is then trained using the set of labels. Following training, the model is provided for detection of the objects of interest.