Microscope Controller Wound Field Adjustment
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Solution Overview
Problem
Surgeons face challenges in manually adjusting microscopes during surgeries, particularly in deep wounds, as manual adjustments are slower and less reliable, diverting attention from the surgical procedure and potentially compromising surgical outcomes.
Innovation Solution
A controller for microscopes that uses image data to automatically adjust the field of view by identifying wounds based on depth profiles, spectral content, hemoglobin presence, oxygen saturation, polarization, and color ranges, allowing for precise and efficient alignment without additional hardware, and utilizing a trained artificial neuronal network for accurate identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual adjustment of the microscope is used, then the surgeon can control the field of view, but the adjustment is slower and diverts attention from the surgical procedure
Solution Approach 1:
The microscope system performs self-adjustment by automatically detecting the wound area in real-time and autonomously modifying its field of view parameters. The control unit processes images from the imaging device and adjusts the microscope without requiring surgeon intervention, allowing the system to serve itself rather than relying on manual operation.
Solution Approach 2:
The patent replaces the mechanical manual adjustment system with an automated control system that uses image processing algorithms to detect wound characteristics and automatically modifies microscope parameters. This substitution eliminates the need for physical manual manipulation of focus and field of view controls.
2Productivity
If automated adjustment is implemented, then the adjustment speed increases, but additional hardware may be required
Solution Approach 1:
The control unit serves multiple functions: it processes images from the imaging device, detects wound areas using image analysis algorithms, determines optimal field of view parameters, and executes microscope adjustments. By consolidating these functions into a single control unit that already exists in modern microscopes, the system avoids requiring separate dedicated hardware components.
Solution Approach 2:
The control unit acts as an intermediary between the imaging device and the microscope's field of view control mechanisms. It receives image data, processes it through wound detection algorithms, and translates the results into control signals for the microscope, eliminating the need for direct complex hardware connections between imaging and focusing systems.
3Measurement precision
If wound identification uses multiple parameters (depth, spectral content, hemoglobin), then identification accuracy improves, but computational complexity increases
Solution Approach 1:
The wound detection process is segmented into distinct analytical stages: first detecting depth information from stereo or focus variation images, then analyzing spectral content and hemoglobin presence in separate processing steps. This segmentation allows the control unit to process different parameters independently and combine results, reducing overall computational complexity compared to simultaneous multi-parameter analysis.
Solution Approach 2:
The system implements progressive wound identification by initially using simpler parameters like depth and color information for quick detection, then optionally refining the identification using more complex spectral and hemoglobin analysis only when needed. This partial action approach achieves sufficient accuracy for most cases while reducing computational burden.
Data Source
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Figure 3
AI summary
A controller for a microscope is configured to receive image data representing tissue and to identify a wound using the image data. Further, the controller is configured to output a control signal for the microscope, the control signal instructing the microscope to adjust its field of view to the wound.