Biological Sample Imaging and Manipulation via 3D Model Reconstruction
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Solution Overview
Problem
Current biomedical imaging systems face challenges in efficiently processing images of biological samples for accurate manipulation, particularly in removing noise and blurs, and reconstructing high-quality 3D models for precise intracellular operations.
Innovation Solution
A system comprising an imaging device, a controller, and a tool manipulation device, which uses deconvolution and segmentation methods to process fluorescence images from a wide-field fluorescence microscope, allowing for precise 3D model reconstruction and controlled tool movement for intracellular manipulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deconvolution and segmentation methods are used to process fluorescence images, then image quality and 3D model accuracy are improved, but processing time and computational complexity increase
Solution Approach 1:
The system performs deconvolution and segmentation operations in advance on the fluorescence images before manipulation operations are executed. By pre-processing the images to remove noise and blurs, and reconstructing 3D models beforehand, the system reduces real-time processing requirements during actual manipulation tasks, thereby improving image quality while managing processing time through advance preparation
Solution Approach 2:
The patent replaces manual image processing and analysis with automated computational algorithms. The controller automatically performs deconvolution, segmentation, and 3D reconstruction operations, substituting manual mechanical processing with efficient computational methods that reduce processing time while maintaining or improving image quality and measurement precision
2Manufacturing precision
If automated control based on processed images is implemented, then manipulation precision is improved, but system complexity increases
Solution Approach 1:
The controller serves multiple functions within the system: it processes fluorescence images through deconvolution and segmentation algorithms, reconstructs 3D models of the biological sample, analyzes the processed images to determine manipulation parameters, and controls the tool manipulation device. By consolidating these diverse functions into a single multi-functional controller, the system achieves high manipulation precision while managing complexity through functional integration rather than proliferation of separate components
Solution Approach 2:
The system implements a closed-loop feedback mechanism where the controller continuously processes images from the imaging device, analyzes the results, and adjusts tool manipulation accordingly. The processed image data feeds back to guide manipulation operations, enabling precise control while the automated feedback loop reduces the need for complex manual intervention systems
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system effectively removes noise and blurs from images, enabling accurate 3D model reconstruction and improving the success rate of intracellular surgeries by providing reliable 3D position information, reducing operation damage and outperforming manual control.
Implementation Method 1
the images are fluorescence images obtained with a fluorescence microscope
Data Source
AI summary
A system for manipulating a biological sample, and a method for operating such a system. The system includes an imaging device arranged to image a biological sample; a controller operably connected with the imaging device for processing the images obtained by the imaging device; and a tool manipulation device operably connected with the controller and arranged to be connected with a tool for manipulating the biological sample. The controller is arranged to control operation of the tool manipulation device based on the processing of the images.