Microscope Workflow Adaptation Using Trained Models
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Solution Overview
Problem
Existing microscopes face challenges in efficiently handling specimens with high variability, requiring extensive data recording and storage due to preprogrammed schemes that may miss important biological phenomena, limited application support, ineffective image processing feedback, and unpredictable device malfunctions leading to high costs and time wastage.
Innovation Solution
Implementing a method and apparatus that utilize trained models, particularly neural networks, to optimize workflows by capturing data and adapting models based on specimen variability, enabling precise predictions and continuous model updates through data aggregation and user feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If preprogrammed schemes are used for microscope measurements, then the workflow can be automated, but important biological phenomena may be missed and too much non-informative data is recorded
Solution Approach 1:
The patent implements feedback mechanisms where measurement results are continuously evaluated and fed back into the workflow control. Trained models assess recorded data in real-time and dynamically adjust measurement parameters, allowing the system to respond to unexpected biological phenomena and adapt the workflow accordingly, thus preventing information loss while maintaining automation.
Solution Approach 2:
The patent transforms static preprogrammed schemes into dynamic workflows that can adapt during measurement. The system uses trained models to continuously evaluate data and automatically adjust measurement parameters, timing, and focus based on what is observed, enabling the workflow to respond flexibly to unexpected phenomena without requiring complete reprogramming.
2Reliability
If preprogrammed schemes record comprehensive data to ensure coverage of all phenomena, then no biological phenomena are missed, but data storage costs and evaluation time increase significantly
Solution Approach 1:
The patent applies partial action by using trained models to evaluate data in real-time and selectively continue or terminate measurements based on what has already been observed. Instead of recording all possible data comprehensively, the system performs just enough measurement to capture the necessary biological phenomena, reducing data storage requirements while maintaining reliability through intelligent monitoring.
3Measurement precision
If conventional image processing feedback is used, then image recording conditions can be captured, but the accuracy is low and it can only be used for specific specimen cases
Solution Approach 1:
The patent employs parameter changes by utilizing trained models that can process and adapt to various specimen types through learned parameters. Instead of conventional image processing with fixed thresholds and parameters, the trained models adjust their internal parameters based on the specific specimen being examined, enabling high accuracy across diverse specimen types rather than being limited to specific cases.
4Measurement precision
If neural networks are used for postprocessing of recorded images, then image quality can be improved, but information from original images is lost and workflow intervention during measurement is not possible
Solution Approach 1:
The patent applies preliminary action by using trained models to predict optimal measurement parameters and conditions before the actual measurement takes place. This allows the system to prepare and adjust settings in advance based on preliminary analysis, improving image quality from the outset rather than relying on postprocessing that would lose original information. The workflow can also be intervened upon during measurement based on these preliminary predictions.
5Reliability
If device parameters deviate from norm during experiment, then the experiment must be repeated entirely, causing great costs and time outlay
Solution Approach 1:
The patent implements continuity of useful action by continuously monitoring device parameters throughout the experiment using trained models. When deviations are detected, the system can automatically adjust parameters or alert the user immediately, allowing the experiment to continue with corrected parameters rather than requiring complete repetition. This maintains result validity while avoiding time loss from restarting experiments.
6Adaptability or versatility
If new applications or specimens with great variability are examined, then the system can handle diverse cases, but more time outlay is required to develop image processing from bottom up
Solution Approach 1:
The patent applies preliminary action through pre-trained models that have been trained on diverse specimen types in advance. When a new application or specimen type is encountered, the system can leverage these pre-trained models as a starting point rather than developing image processing algorithms from scratch. This significantly reduces development time while maintaining the ability to handle diverse specimens, as the pre-trained models provide a head start that can be fine-tuned for specific new applications.
Data Source
AI summary
A method for optimizing a workflow of at least one microscope or microscope system includes a step a) of implementing a workflow by one or more components of at least one microscope and/or microscope system, wherein the workflow comprises a capture of first data. In a step b), a trained model is determined for the workflow, at least in part based on the captured first data.