Automated Microscope Navigation for Sequential Slice Imaging
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Solution Overview
Problem
Current methods for automatic microscopic image acquisition of sequential slices face challenges such as unstable quality, low space utilization, and inconsistent azimuth angles in slice collection, making it difficult to effectively acquire images of interested sample regions.
Innovation Solution
A method involving image processing and machine learning techniques to identify and label sequential slices, establish a coordinate transformation matrix, and navigate the microscope to locate and acquire images of sample points, ensuring accurate and automatic image acquisition of sequential slices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual slice collection is used, then collection speed and flexibility are improved, but slice distribution density becomes large and azimuth angle consistency deteriorates
Solution Approach 1:
The patent replaces manual mechanical slice collection with an automated system that uses image processing and machine learning algorithms to identify, locate, and navigate to sample regions. The system automatically processes navigation images to determine slice positions and coordinates, eliminating manual intervention while maintaining both speed and precision.
Solution Approach 2:
The system performs self-service by automatically analyzing navigation images, identifying sample regions, calculating coordinates, and guiding the microscope without human intervention. The automated image processing and machine learning models enable the system to independently complete the entire slice collection process.
2Ease of operation
If automatic slice collection is used, then uniform density and labor economy are improved, but slice quality stability and space utilization deteriorate
Solution Approach 1:
The patent replaces automatic mechanical slice collection with an intelligent system that uses machine learning models to analyze navigation images and determine optimal slice positions. This substitution enables the system to maintain uniform density while improving quality stability through intelligent image analysis and adaptive positioning.
Solution Approach 2:
The system dynamically adjusts collection parameters based on image analysis results, including slice position coordinates, magnification levels, and acquisition parameters. By changing parameters adaptively based on actual sample characteristics, the system improves both quality stability and space utilization while maintaining uniform density.
3Extent of automation
If navigation and location methods are established for sequential slices, then automatic image acquisition of interested regions is improved, but system complexity increases
Solution Approach 1:
The patent replaces complex mechanical navigation systems with software-based image processing and coordinate transformation algorithms. By using machine learning models to analyze navigation images and calculate coordinates computationally, the system achieves automatic image acquisition without requiring complex physical navigation mechanisms.
Solution Approach 2:
The system creates a digital representation (navigation image) of the sample landscape and performs all navigation operations on this copy rather than directly manipulating physical coordinates. This copying approach simplifies the navigation system by working with image data and calculated coordinates instead of complex mechanical positioning.
Data Source
AI summary
A method for microscopic image acquisition based on a sequential slice. The method includes; acquiring a sample of the sequential slice and a navigation image thereof; identifying and labeling the sample of the sequential slice in the navigation image by utilizing methods of image processing and machine learning; placing the sample of the sequential slice in a microscope, establishing a coordinate transformation matrix for a navigation image-microscope actual sampling space coordinate, and navigating and locating a random pixel point in the navigation image to a center of the microscope's visual field; locating the sample of the sequential slice under a low resolution visual field, binding a sample acquisition parameter; based on the binding of the sample acquisition parameter, recording a relationship of relative of locations between a center point of a high resolution acquisition region and a center point after being matched with a sample template.