Surgical Microscope Auto-Configuration Using Stereo Target Detection
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Solution Overview
Problem
The process of setting microscope parameters for surgical microscopes, such as position, focus, and magnification, is time-consuming and demanding, requiring manual adjustment before surgery.
Innovation Solution
Utilizing trained machine learning methods and computer vision evaluation to automatically identify surgical targets and estimate optimal microscope parameters based on captured left and right image representations, controlling actuators to adjust the microscope accordingly without manual selection or marking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual configuration of microscope parameters is used, then the surgeon can precisely adjust settings, but the setup time becomes excessive and the process is demanding
Solution Approach 1:
The system performs preliminary actions by automatically identifying the target object and pre-calculating optimal microscope parameters (focus, magnification, centering) before the surgeon begins the procedure. The machine learning model has been trained in advance on surgical images to rapidly determine configuration settings, eliminating the need for time-consuming manual adjustment while maintaining precision.
Solution Approach 2:
The surgical microscope system performs self-configuration through automated target detection and parameter optimization. The control unit automatically adjusts microscope settings based on image analysis without requiring manual intervention, enabling the system to service itself in terms of configuration while the surgeon focuses on the surgical task.
2Productivity
If automated parameter setting is implemented, then setup time is reduced, but the complexity of the system increases due to machine learning components
Solution Approach 1:
The patent introduces a control unit as an intermediary between the imaging system and the microscope adjustment mechanisms. This control unit contains the machine learning model that processes images and translates them into parameter adjustments. The intermediary handles the complexity of automated decision-making, allowing the rest of the system to remain relatively simple while achieving high configuration speed through intelligent processing.
3Ease of operation
If manual target selection and marking is performed, then the surgeon has full control over target identification, but the process requires multiple preliminary steps and marking actions
Solution Approach 1:
The system performs self-service by automatically detecting and identifying the target object in the surgical field without requiring the surgeon to manually mark or select it. The machine learning model analyzes the captured images to locate anatomical structures or pathological features, automatically determining the target position and characteristics, thereby eliminating multiple preliminary marking steps while maintaining surgical control.
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.