Microscope Setup Automation Using Image and Parameter Embeddings
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Solution Overview
Problem
Modern biomedical imaging instruments require significant domain expertise for proper operation, leading to potential errors and degraded image quality due to complex setups and varied terminology among manufacturers, limiting user capability and efficiency.
Innovation Solution
A method utilizing a pre-trained image model to predict embeddings of instrument parameters and compare them with pre-computed values to assist in setting up imaging instruments, providing automated adjustments and recommendations for better image quality without extensive human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual operation of imaging instrument is used, then user can control instrument settings, but user requires significant domain expertise and setup time increases
Solution Approach 1:
The system performs self-service by automatically analyzing the acquired image and adjusting instrument parameters without requiring manual intervention. The AI model evaluates image quality metrics and autonomously optimizes settings, eliminating the need for users to have extensive domain expertise or spend time manually adjusting parameters.
Solution Approach 2:
The system performs preliminary actions by pre-computing embeddings for various instrument parameter combinations during system initialization. These pre-computed embeddings are stored and ready for rapid comparison with actual image embeddings during operation, enabling fast parameter optimization without time-consuming real-time calculations.
2Reliability
If manual operation of imaging instrument is used, then user can control instrument settings, but potential errors increase and image quality degrades
Solution Approach 1:
The system implements feedback by continuously monitoring image quality metrics and using the AI model to evaluate whether current parameter settings are optimal. Based on this feedback, the system automatically adjusts parameters to improve image quality, reducing errors that would otherwise occur due to manual operation limitations.
Solution Approach 2:
The patent replaces manual mechanical adjustment with an automated AI-based system. The AI model substitutes human decision-making with algorithmic analysis of image embeddings and parameter relationships, eliminating human error while maintaining ease of operation through automated control.
3Productivity
If automated parameter optimization is implemented, then setup time decreases and image quality improves, but system complexity increases
Solution Approach 1:
The system introduces an intermediary layer consisting of pre-computed parameter embeddings and an AI evaluation model. This intermediary translates complex parameter optimization problems into simpler embedding space comparisons, enabling automated optimization without requiring the entire system to become overly complex. The intermediary handles the computational complexity while presenting a simple interface for image acquisition.
4Manufacturing precision
If domain expertise is required for operation, then image quality can be optimized, but number of capable users decreases
Solution Approach 1:
The system performs self-service by automatically compensating for the lack of user expertise. The AI model embedded in the system possesses the domain knowledge required for optimization, eliminating the need for individual users to acquire extensive domain expertise. Any user can operate the system and achieve optimized image quality through automated parameter adjustment.
Data Source
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AI summary
A method (100) of operating an imaging instrument comprises using an image as an input for a pre-trained image model (110), the pre-trained image model generating a prediction of embeddings of keys and associated predicted values as an output. The method (100) further comprises comparing the prediction of the embeddings of the keys and the associated predicted values of the pretrained image model with pre-computed embeddings of the keys and the associated values used to generate the image (120). The method (100) comprises generating information on a quality of the values used to generate the image (130).