Microscopy System Machine Learning Overview Image Generation
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Solution Overview
Problem
Microscopy systems face challenges in achieving optimum image quality for overview images due to varying ambient and measurement conditions, particularly with diverse sample types and lighting conditions, which complicates automated sample identification and collision prevention.
Innovation Solution
A microscopy system that captures multiple raw overview images with different capture parameters and utilizes a machine learning model trained on diverse image datasets to select or combine images for optimal output, determining suitable parameters and assessing image quality based on sample areas and contextual data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple raw overview images are captured with different capture parameters, then the quality and reliability of the overview image is improved, but the complexity of image processing and parameter selection increases
Solution Approach 1:
The machine learning model automatically evaluates and selects the optimal overview image from multiple captured images with different parameters, eliminating the need for manual parameter tuning and image selection. The system self-optimizes by learning from training data what constitutes a high-quality overview image under various conditions.
Solution Approach 2:
The system captures multiple raw overview images with varied capture parameters (exposure time, gain, illumination intensity) and uses a machine learning model to evaluate these parameter variations. The model learns optimal parameter combinations from training data, automatically adapting to different sample types and lighting conditions without requiring explicit rule-based parameter management.
2Adaptability or versatility
If manual assessment criteria are used to select the best overview image, then adaptability to different sample types is improved, but the automation and processing speed deteriorate
Solution Approach 1:
The patent replaces manual image assessment and selection processes with an automated machine learning model. The model has been trained on diverse training images representing various sample types, lighting conditions, and capture parameters, enabling it to automatically evaluate and select optimal overview images without human intervention while maintaining adaptability across different sample types.
Solution Approach 2:
The machine learning model is pre-trained on a comprehensive dataset of training images that cover various sample types, lighting conditions, and capture parameters. This preliminary training enables the model to automatically adapt to new sample types and conditions without requiring manual reconfiguration or assessment criterion development for each new scenario.
3Measurement precision
If explicit rules are programmed for image processing, then processing precision is improved, but the system's ability to handle diverse conditions deteriorates
Solution Approach 1:
Instead of using fixed explicit rules for image quality assessment, the system employs a machine learning model that learns optimal assessment criteria from training data. The model can adapt its evaluation parameters based on the specific characteristics of different sample types, lighting conditions, and capture parameters, maintaining precision while handling diversity.
Solution Approach 2:
The image quality assessment transitions from static explicit rules to dynamic learned criteria. The machine learning model adapts its assessment behavior based on the input image characteristics, learning to weight different quality factors differently depending on the sample type and capture conditions, thereby maintaining precision across diverse conditions.
Data Source
AI summary
A microscopy system comprises a microscope and a computing device which is configured to control the microscope to capture a plurality of raw overview images showing the sample environment with different capture parameters. The computing device comprises a machine learning model trained with training raw overview images, which receives the raw overview images as input and generates therefrom an output which is or which defines the overview image.