Microscope Focus Repositioning Using ML Signatures for Thick Samples
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Solution Overview
Problem
Existing methods for stabilizing the focus position of imaging devices, such as microscopes, fail to reliably maintain focus on thick and time-variable samples without increasing costs or mechanical rigidity, and are limited by factors like sample movement and environmental changes.
Innovation Solution
A method using a machine learning model to determine and adjust the focus position based on signature distances derived from images, allowing the imaging device to reposition itself to maintain sharp focus on target sample structures by adapting the focus position dynamically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If mechanical stability is increased by stiffening the microscope, then focus position stability is improved, but weight and costs increase and flexibility with respect to samples is reduced
Solution Approach 1:
The patent replaces mechanical stabilization methods with a software-based focus holding system. The evaluation device uses image analysis algorithms to detect focus position drift and automatically adjusts the focus position through software control, eliminating the need for mechanically stiffening the microscope structure. This substitution resolves the contradiction by achieving focus stability through intelligent control rather than mechanical rigidity.
Solution Approach 2:
The focus holding system operates autonomously by continuously monitoring image quality metrics and self-correcting focus position drift without external intervention. The evaluation device automatically detects when focus has drifted and adjusts it back to the optimal position, enabling the system to maintain focus stability through self-service rather than requiring complex mechanical stabilization structures.
2Reliability
If hardware-based holding focus is used, then focus position stability is improved, but additional modules and costs are required
Solution Approach 1:
The patent substitutes hardware-based holding focus modules with a software-based evaluation device that analyzes images to determine focus position. Instead of using additional optical modules like gratings and infrared light sources, the system uses image processing algorithms to detect focus drift and correct it, eliminating the need for expensive hardware add-ons while maintaining focus stability.
Solution Approach 2:
The system creates a virtual model of the focus position relationship by analyzing image characteristics and learning the correlation between image quality metrics and focus position. This virtual copy allows the system to predict and correct focus drift without requiring physical hardware modules, achieving the same effect through data-driven methods.
3Device complexity
If software-based holding focus is used, then costs are reduced, but robustness and reproducibility are reduced for thick and time-variable samples
Solution Approach 1:
The patent implements a continuous feedback loop where the evaluation device constantly monitors image quality metrics and compares them against reference values to detect focus drift. The system uses this feedback information to automatically adjust the focus position, ensuring robust performance even for thick and time-variable samples. This feedback mechanism enables the simple software-based system to achieve high reliability by continuously adapting to sample changes.
Solution Approach 2:
The system performs preliminary actions by establishing reference focus positions and image quality metrics before observing the sample. These pre-established references serve as benchmarks for detecting and correcting focus drift during the experiment, enabling the software-based system to maintain robustness without requiring complex hardware modifications.
4Reliability
If mechanical stability is increased, then focus position stability is improved, but access to sample holder is made more difficult
Solution Approach 1:
The patent replaces mechanical stabilization structures with a software-based focus control system. This substitution eliminates the need for bulky mechanical stiffening components that would obstruct access to the sample holder, allowing researchers to easily load and manipulate samples while the software system maintains focus stability during observation.
Data Source
AI summary
A method for repositioning a focus position of an imaging device in a target focus position in a sample in an experiment comprises: defining the target focus position, repositioning a current focus position based on the target focus position, comprising determining one or more compare signatures based on a current focus position, determining one or more distances in each case between the compare signatures and a target signature based on the target focus position, adapting the current focus position based on the distances. A signature is an output of a machine learning model corresponding to a focus position and based on an image of the sample recorded with the focus position, and the target focus position is a focus position in the sample in which the imaging device captures a target image of the sample and the machine learning model outputs the target signature when the target image is input.