Microscope Light Beam Spatial Control via Virtual Mask
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Solution Overview
Problem
Conventional microscopy methods lack efficient spatial control of laser usage, particularly for complex region of interest (ROI) structures, which can be time-consuming and limiting in applications like in vivo experiments.
Innovation Solution
The method involves automatically generating sample information from a first image to control the modulation of a light beam during scanning, effectively creating a virtual mask to spatially control laser usage, allowing for complex light patterns and efficient ROI definition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual ROI definition is used, then spatial control of laser usage is achieved, but time consumption increases and productivity decreases
Solution Approach 1:
The system automatically defines ROIs by analyzing the sample image and identifying structures of interest, eliminating the need for manual user input. The software autonomously segments the image, detects relevant structures, and generates binary masks that control laser application, allowing the system to serve itself rather than requiring continuous user intervention
Solution Approach 2:
The manual mechanical process of drawing ROIs with mouse or stylus is replaced by an automated image processing system using algorithms for image segmentation and structure detection. The mechanical interaction between user and interface is substituted with computational analysis that automatically identifies and defines regions of interest based on image content
2Manufacturing precision
If complex ROI structures are defined manually, then precise spatial control is achieved, but the process becomes time-consuming and limits application scope
Solution Approach 1:
The system performs preliminary image analysis and automatic ROI definition before the actual imaging experiment begins. By pre-processing the sample image to identify structures and generate binary masks in advance, the system prepares all spatial control parameters beforehand, eliminating time consumption during the experiment itself while maintaining precise spatial control
Solution Approach 2:
The system transforms the complex manual parameter adjustment process into automated parameter generation through image analysis algorithms. By changing from manual coordinate input to algorithmic parameter derivation based on image features, the system achieves both high precision and time efficiency simultaneously
3Ease of operation
If binary masks are used for spatial control, then light beam modulation is achieved, but system complexity increases
Solution Approach 1:
Binary masks serve as an intermediary data structure that simplifies the interface between image analysis and light beam control. Instead of directly controlling complex laser parameters, the system uses simple binary masks (0 or 1 values) as a mediator that translates image analysis results into straightforward on/off control signals for the light beam, reducing operational complexity
Solution Approach 2:
The system extracts only the essential spatial information needed for control by reducing the image data to binary masks that contain only the critical on/off regions. By taking out and isolating the most important spatial patterns while discarding unnecessary detail, the system simplifies the control mechanism while maintaining effective spatial control capability
Data Source
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Figure 2a~2d
AI summary
A method for imaging a sample using a microscope comprises the steps of: recording a first image of the sample, the first image being represented by image data; extracting sample information from the first image by analyzing the image data using an analyzer configured to analyze the image data; scanning at least a part of the sample with a light beam while modulating the light beam based on the extracted sample information; and recording a second image of the sample during and/or after scanning the sample with the modulated light beam.