Endoscope ISP Adaptation for Scene-Adaptive Surgical Video
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Solution Overview
Problem
Endoscopes used in surgical procedures face significant variations in video quality due to changing illumination and anatomical features, leading to suboptimal image settings that can hinder surgical performance.
Innovation Solution
A system employing machine learning models to recognize surgical scenes and features, dynamically adjusting image signal processing parameters in real-time to maintain high video quality by tailoring ISP settings to specific conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual controls are provided for adjusting image settings, then the surgeon can customize brightness and contrast, but the system complexity increases and real-time adaptation is difficult
Solution Approach 1:
The system automatically analyzes surgical scenes and adjusts ISP parameters without surgeon intervention. The machine learning model processes video frames to identify scene types and automatically selects optimal brightness, contrast, and other image settings, eliminating the need for manual controls while maintaining adaptability.
Solution Approach 2:
The system dynamically changes ISP parameters based on detected scene characteristics. The machine learning model identifies scene types (e.g., smoke-filled, bleeding, different anatomical structures) and automatically adjusts corresponding image processing parameters to optimize visualization for each specific surgical condition.
2Reliability
If static ISP parameters are used, then the system is simple to operate, but video quality varies significantly under different illumination and anatomical conditions
Solution Approach 1:
The system transitions from static to dynamic ISP parameter adjustment. The machine learning model continuously analyzes incoming video frames and dynamically modifies image processing parameters in real-time based on changing surgical conditions, ensuring consistent video quality across varying illumination and anatomical scenarios.
Solution Approach 2:
The system implements a feedback loop where the machine learning model continuously evaluates the current surgical scene and adjusts ISP parameters accordingly. The analysis of video frames provides feedback about scene conditions, which automatically triggers parameter adjustments to maintain optimal image quality throughout the procedure.
3Reliability
If scene analysis is performed to adjust image settings, then video quality improves under varying conditions, but processing time and computational resources increase
Solution Approach 1:
The machine learning model is pre-trained on extensive surgical video datasets before deployment. This preliminary training enables the model to quickly recognize scene types and adjust parameters in real-time during surgery without requiring complex computational resources or extensive processing time during the actual procedure.
Solution Approach 2:
The system performs scene analysis at strategically selected intervals rather than continuously processing every single frame. By analyzing a representative subset of frames and maintaining parameter settings between analyses, the system achieves good image quality adaptation while reducing computational overhead and processing time.
Data Source
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AI summary
One example method for scene-adaptive image quality in surgical video includes receiving a first video frame from an endoscope, the first video frame generated from a first raw image captured by an image sensor of the endoscope and processed by an image signal processing ("ISP") pipeline having a plurality of ISP parameters; recognizing, using a trained machine learning ("ML") model, a first scene type or a first scene feature type based on the first video frame; determining a first set of ISP parameters based on the first scene type or the first scene feature type; applying the first set of ISP parameters to the ISP pipeline; and receiving a second video frame from the endoscope, the second video frame generated from a second raw image captured by the image sensor and processed by the ISP pipeline using the first set of ISP parameters.