MoE Transformer Video Processing for Real-Time Surgical Segmentation
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Solution Overview
Problem
Existing computer vision applications for video processing, such as object tracking and instance segmentation, are computationally expensive and not suitable for real-time use, particularly in resource-constrained environments like computer-assisted surgery systems.
Innovation Solution
A spatial-temporal modular network (STMN) using a mixture of experts (MoE) transformer encoder with batch priority routing to generate frame predictions, dynamically controlling computational resources and reducing redundancy in video frames.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing computer vision applications use traditional processing methods for video analysis, then measurement precision and reliability are improved, but device complexity and computational resource requirements increase significantly
Solution Approach 1:
The video processing system is segmented into multiple expert networks, each specialized in processing specific types of video frames or surgical scenarios. The router dynamically directs frames to appropriate experts, dividing the complex processing task into manageable specialized sub-tasks that maintain accuracy while reducing overall computational burden.
Solution Approach 2:
The system dynamically changes processing parameters based on frame characteristics. The router analyzes frame content and adjusts which expert networks are activated, modifying computational resources allocated to each frame based on its specific requirements rather than applying uniform high-complexity processing to all frames.
2Productivity
If traditional video processing methods are used to ensure real-time performance, then productivity is improved, but measurement precision and segmentation accuracy deteriorate
Solution Approach 1:
The system dynamically adapts its processing architecture in real-time based on incoming frame characteristics. The router continuously evaluates frame content and dynamically routes frames to appropriate expert networks, allowing the system to maintain high processing speed while achieving high segmentation accuracy when needed.
Solution Approach 2:
Processing parameters such as which expert networks are activated are dynamically changed based on frame requirements. This allows the system to use lightweight processing for simple frames (maintaining speed) and activate full expert networks only when complex segmentation is needed (maintaining accuracy).
3Measurement precision
If comprehensive video frame processing is performed to improve segmentation accuracy, then measurement precision is improved, but energy consumption and computational load increase
Solution Approach 1:
The system extracts and processes only the essential features of video frames using specialized expert networks. Rather than performing comprehensive processing on all frames, the router identifies and extracts only the frames and features that require detailed analysis, reducing overall energy consumption while maintaining segmentation accuracy for critical frames.
Solution Approach 2:
The system changes processing parameters dynamically, activating full processing only when necessary. The router adjusts which expert networks are engaged based on frame content, reducing energy consumption by using minimal processing for simple frames and reserving full computational resources for frames requiring high segmentation accuracy.
4Device complexity
If resource-constrained systems are used for surgical video processing, then device complexity is reduced, but productivity and real-time processing capability deteriorate
Solution Approach 1:
The processing system is segmented into multiple specialized expert networks that can be selectively activated. This segmentation allows resource-constrained systems to run only the necessary experts for each frame rather than maintaining a single complex processing pipeline, reducing overall system complexity while preserving real-time capability through efficient resource utilization.
Solution Approach 2:
The system dynamically changes which processing components are active based on frame requirements. This parameter change strategy allows resource-constrained systems to maintain real-time processing capability by activating only the necessary processing power for each frame, avoiding the need for continuously high computational resources.
Data Source
AI summary
Examples described herein provide a computer-implemented method that includes generating temporal prompts based on a video frame history. The method further includes generating, using a mixture of experts (MoE) transformer encoder, the frame prediction for the frame of the video of the surgical procedure based on the frame of the video of the surgical procedure and the temporal prompts.