Automated Microscope Calibration via Image Recognition
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Solution Overview
Problem
Current microscope calibration methods are inefficient and prone to errors due to manual operation, lack of reproducibility, and require skilled personnel, leading to increased costs and time consumption in maintaining and calibrating systems.
Innovation Solution
A computer-implemented method and system that automatically identifies and classifies calibration samples using image recognition, selecting the appropriate calibration workflow based on trained classification systems, eliminating the need for manual triggering and ensuring correct alignment and adaptation to system configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual calibration methods are used, then calibration can be performed with simple equipment, but the process is time-consuming and requires skilled personnel
Solution Approach 1:
The calibration sample automatically triggers the calibration workflow when detected by the imaging system. The system autonomously identifies the calibration sample, determines the appropriate calibration type, and initiates the calibration sequence without requiring manual intervention or skilled operator knowledge.
Solution Approach 2:
Manual mechanical operations for calibration are replaced by an automated image recognition and processing system. The imaging system captures images of the calibration sample, processes them through algorithms to identify calibration features, and automatically executes the calibration workflow, eliminating the need for manual positioning and operation.
2Measurement precision
If manual calibration operations are performed, then operational flexibility exists, but reproducibility and accuracy are compromised
Solution Approach 1:
The system continuously monitors the calibration process by capturing images of the calibration sample, analyzing the position and characteristics of calibration features, and providing feedback to adjust the calibration workflow. This closed-loop feedback ensures accurate calibration while maintaining operational simplicity through automated adjustment.
Solution Approach 2:
The system automatically adjusts calibration parameters such as focus position, illumination intensity, and image capture settings based on the detected calibration sample characteristics. This automated parameter optimization ensures consistent accurate measurements without requiring manual tuning or complex operational procedures.
3Reliability
If calibration is performed regularly to maintain system performance, then measurement reliability is improved, but time and staff resources are consumed
Solution Approach 1:
The calibration sample is pre-prepared with known calibration features and markings that enable automatic detection. The calibration workflow is pre-programmed in the system, so when the calibration sample is introduced, the entire calibration sequence is automatically initiated and executed without requiring time-consuming manual setup or operator intervention.
4Measurement precision
If skilled personnel are required for calibration, then calibration quality is maintained, but operational costs increase
Solution Approach 1:
The system creates a digital copy of the calibration sample through image capture and stores it for analysis. The calibration information is encoded in the image data and processed by automated algorithms, eliminating the need for skilled personnel to physically handle and interpret calibration samples. This digital copying approach maintains calibration quality while significantly reducing operational costs.
Data Source
AI summary
A method and a corresponding calibration system for calibrating a microscope system involve a recording of an overview image of a sample stage of the microscope system and an identification of a calibration sample in the recorded overview image. Moreover, The calibration sample in the recorded overview image is classified into one of a plurality of calibration sample classes using a classification system, which was trained using training data, in order to form a model so that the classification system is adapted for classifying unknown input data into prediction classes. A of a calibration workflow for calibrating the microscope system is selected based on the classified calibration sample class. The selection is performed using a workflow indicator value serving as an input value for a workflow selection system.