Automated Optical Training System for Microscope Sample Evaluation
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Solution Overview
Problem
Current methods for obtaining ground truth in machine learning, particularly for microscopic systems, are time-consuming and often require manual intervention, especially in segmenting samples and combining data from different sources.
Innovation Solution
A method that automates the detection of sample positions using optical recording systems, allowing for the generation of training information without manual intervention, by differing in exposure times and using machine learning techniques to determine additional information, which can then be used to train evaluation tools for subsequent applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual procedures are used for segmentation and ground truth determination, then measurement precision is improved, but productivity deteriorates
Solution Approach 1:
The system uses automated algorithms to perform segmentation and ground truth determination without manual intervention. The training system automatically processes samples, performs segmentation, and generates ground truth data, enabling the system to serve itself and eliminating the need for manual labor while maintaining consistent quality standards.
Solution Approach 2:
Manual mechanical segmentation processes are replaced with automated computational algorithms. The patent substitutes human operators with computer-based image processing and machine learning algorithms that automatically perform segmentation tasks, thereby increasing productivity while maintaining or improving measurement precision through consistent algorithmic application.
2Measurement precision
If complex data processing procedures are used to combine data from different sources, then measurement precision is improved, but device complexity deteriorates
Solution Approach 1:
The training system is designed as a multi-functional platform that can handle multiple data sources, perform various processing operations, and generate different types of training data. By consolidating these functions into a single universal system, the patent reduces the need for multiple separate complex devices while maintaining the ability to process diverse data sources effectively.
Solution Approach 2:
The patent combines multiple data processing functions and operations into an integrated workflow within a single training system. By merging data acquisition, processing, segmentation, and ground truth generation into one unified system, the complexity is consolidated and managed more efficiently rather than distributed across multiple separate complex devices.
3Productivity
If automated detection is used for sample positions, then productivity is improved, but measurement precision deteriorates
Solution Approach 1:
The automated detection system incorporates feedback mechanisms that continuously monitor and adjust detection parameters based on detected sample positions. The system uses the detected position information to refine subsequent detection operations, ensuring that automation maintains high precision while achieving fast processing speeds through iterative optimization.
Data Source
AI summary
The invention concerns a method for providing an evaluation means (60) for at least one optical application system (5) of a microscope-based application technology,wherein the following steps are performed, in particular each by an optical training system (4):performing an input detection (101) of at least one sample (2) according to the application technology in order to obtain at least one input record (110) of the sample (2) from the input detection (101),performing a target detection (102) of the sample (2) according to a training technology to obtain at least one target record (112) of the sample (2) from the target detection (102), the training technology being different from the application technology at least in that additional information (115) about the sample (2) is provided,training (130) of the evaluation means (60) at least on the basis of the input recording (110) and the target recording (112), in order to obtain a training information (200) of the evaluation means (60),in that various sample positions are automatically detected during the input detection (101) and/or during the target detection (102) so that, in particular, the training information (200) for a continuous relative movement of the sample is trained to determine the additional information (115).