Dynamic Priority Region Image Registration for Ophthalmic Surgery
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Solution Overview
Problem
Current image registration techniques in ophthalmic surgery face challenges in achieving high-speed and accurate real-time registration due to intensive computational demands, leading to misalignment and poor responsiveness, which is critical for delicate procedures like vitreoretinal surgery.
Innovation Solution
An ophthalmic imaging system with an image registration system that dynamically identifies priority registration regions around the distal tip of a surgical instrument, using feature-based or motion-based tracking algorithms, and prioritizes registration within these areas, restricting processing resources to maintain high accuracy and speed without registering non-critical image portions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full-image registration is performed to ensure comprehensive alignment accuracy, then registration precision is improved, but processing time increases and real-time performance deteriorates
Solution Approach 1:
The patent divides the surgical field into multiple regions of interest (ROIs) based on surgical instrument position and surgical step. Instead of registering the entire image, only selected ROIs are registered in real-time, while other regions are updated at lower frequencies or skipped entirely. This segmentation approach maintains registration accuracy in critical areas while dramatically reducing overall processing time and computational load.
Solution Approach 2:
The patent applies different registration qualities and frequencies to different regions of the surgical field. High-priority regions near the surgical instrument receive full real-time registration with highest accuracy, while peripheral or less critical regions receive reduced registration updates. This local quality differentiation ensures that computational resources are concentrated where they provide maximum surgical value, resolving the contradiction between comprehensive accuracy and real-time performance.
2Speed
If real-time registration is performed across the entire image to maintain responsiveness, then registration speed is improved, but computational load increases excessively
Solution Approach 1:
The patent extracts and identifies only the critical regions that require real-time registration based on surgical instrument position, surgical step, and predefined importance weights. By taking out only these essential regions from the full image for high-speed processing, the system achieves real-time registration speed in critical areas while avoiding the excessive computational load that would result from processing the entire image at the same speed.
Solution Approach 2:
The patent applies partial action by performing complete real-time registration only on selected regions of interest rather than the entire surgical field. The system determines which regions receive full registration treatment based on their surgical importance, instrument proximity, and step-specific relevance. This partial application of full registration maintains speed in critical zones while reducing overall computational burden.
3Measurement precision
If comprehensive image registration is performed to ensure complete alignment, then registration accuracy is improved, but system complexity increases
Solution Approach 1:
The patent implements dynamic region selection where the set of regions requiring registration changes automatically based on real-time surgical instrument position, detected surgical steps, and updated importance weights. The system dynamically adjusts which ROIs are selected for registration at each time step, rather than using a static comprehensive approach. This dynamic adaptation maintains alignment accuracy in relevant regions while simplifying the system by excluding irrelevant regions from processing.
Solution Approach 2:
The patent incorporates feedback mechanisms where the system continuously monitors surgical instrument position, detects surgical steps, and uses this information to adjust the selection of regions for registration. The importance weight of each region is updated based on feedback from surgical progress detection, ensuring that registration resources are allocated to the most currently relevant areas. This feedback-driven approach maintains accuracy where needed while reducing complexity by adapting to actual surgical conditions rather than processing all regions uniformly.
Data Source
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AI summary
An ophthalmic surgical system includes an imaging system to generate a first image, an imaging system to generate a second image, and an image registration system to receive the first image, receive the second image, track a location of a distal tip of a surgical instrument in the first image, define a priority registration region in the first image comprising a portion of the first image within a predetermined proximity of the distal tip, register the priority registration region in the first image with a corresponding region in the second image, and update registration of the priority registration region in the first image with the corresponding region in the second image in real time as the distal tip is moved, without registering portions of the first or second images that are outside the registration regions.