Surgical Microscope Auto-Configuration Using Stereo Image Recognition
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Solution Overview
Problem
The process of setting microscope parameters for surgical microscopes is demanding and time-consuming, requiring manual adjustments based on the surgical target object, which can be improved for faster and more efficient operation.
Innovation Solution
Utilizing trained machine learning methods and computer vision evaluation to automatically identify surgical target objects and estimate optimal microscope parameters, such as focus, magnification, and centering, by analyzing left and right image representations captured by a stereoscopic surgical microscope.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual configuration of microscope parameters is performed, then parameter setting accuracy is improved, but setup time increases
Solution Approach 1:
The surgical microscope system performs self-configuration by automatically capturing images of the surgical target object, detecting its position and characteristics, and autonomously adjusting microscope parameters such as focus, magnification, and centering. This eliminates the need for manual parameter adjustment while maintaining accurate setup, thereby reducing setup time without sacrificing parameter setting accuracy.
Solution Approach 2:
The patent replaces manual mechanical adjustment of microscope parameters with an automated image processing system. The system uses cameras to capture images, processes them through detection algorithms to identify target object characteristics, and automatically controls actuators to adjust parameters. This substitution of manual mechanical operations with automated sensing and control reduces setup time while maintaining precision.
2Loss of time
If automated parameter estimation is implemented, then setup time is reduced, but system complexity increases
Solution Approach 1:
The patent integrates multiple functions into a unified automated configuration system. The same imaging system used for surgical visualization also serves for target detection and parameter estimation. The control unit handles both image processing and actuator control, consolidating functions that would otherwise require separate systems. This multi-functionality reduces overall system complexity while enabling automated setup.
Solution Approach 2:
The patent introduces an intermediary image processing layer between the camera and the actuator control. This intermediate system captures images, extracts target object information, and translates it into appropriate control commands for the actuators. By introducing this intermediary layer, the system manages complexity through modular function separation, making the automated configuration process more manageable and systematic.
3Measurement precision
If multiple image representations are processed, then target object identification accuracy is improved, but processing time increases
Solution Approach 1:
The patent captures multiple image representations at different positions and magnifications in advance, during the automated configuration phase. These pre-captured images are then processed to identify target object characteristics and determine optimal microscope parameters. By performing the image capture and initial processing during the setup phase rather than during surgery, the system improves identification accuracy without adding time delay to the actual surgical procedure.
Solution Approach 2:
The system captures more image representations than strictly minimum required, including images at multiple positions and magnification levels. This excessive sampling provides redundant information that improves target object identification accuracy through more comprehensive analysis. The additional images serve as backup data and enable more robust detection algorithms to function reliably, justifying the extra processing time during the automated configuration phase.
Data Source
AI summary
A surgical microscope and method including: capturing a left-side image of a capturing region by a left-side camera and a right-side image of the capturing area by a right-side camera; supplying the captured images to at least one trained machine learning method and/or computer vision evaluation method of a control device of the surgical microscope; and identifying a target object on the basis of the captured images by the at least one trained machine learning method and/or computer vision evaluation method, and optimal microscope parameters and/or a change thereof and/or control commands for an actuator system of the surgical microscope are estimated, control commands for the actuator system of the surgical microscope being generated based on the estimated optimal microscope parameters and/or the estimated change in the microscope parameters, and/or the actuator system being controlled corresponding to the generated and/or estimated control commands.