Surgical Image Annotation via Context-Aware Pixel-Level Processing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current surgical systems face challenges in efficiently processing and annotating surgical data in real-time, which hinders effective situational awareness and automated decision-making during surgical procedures.
Innovation Solution
The system employs image processing to identify elements in surgical video frames based on context data, generates annotation data, and inserts it into the video frames on a pixel level, enabling tracking and verification of surgical elements and tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If image processing is used to identify elements in surgical video frames based on context data, then situational awareness is enhanced, but processing time and computational complexity increase
Solution Approach 1:
The system performs preliminary actions by pre-processing surgical video frames to extract key features and pre-identifying potential surgical elements before full annotation is required. This allows the system to have surgical context data ready in advance, reducing the processing time needed during critical surgical moments while maintaining comprehensive situational awareness.
Solution Approach 2:
The image processing task is segmented into multiple stages: initial frame analysis, element detection, context matching, and annotation generation. By dividing the processing into discrete segments that can be executed in parallel or prioritized based on surgical urgency, the system reduces overall processing time while preserving complete situational awareness information.
2Measurement precision
If annotation data is generated and inserted into video frames on a pixel level, then tracking precision is improved, but device complexity increases
Solution Approach 1:
Instead of applying uniform annotation to all pixels, the system applies annotation data selectively to specific regions and elements identified in the surgical video. Each surgical element receives annotation appropriate to its characteristics and importance, reducing overall system complexity while maintaining high tracking precision for critical elements.
Solution Approach 2:
The system uses template-based annotation copying for recurring surgical elements. Once an element type is identified and annotated, the same annotation template can be copied and applied to similar elements, reducing computational complexity while maintaining consistent tracking precision across multiple instances of the same surgical element.
3Measurement precision
If surgical context data is refined and updated based on identified elements, then annotation accuracy is improved, but processing time increases
Solution Approach 1:
The system implements feedback loops where identified elements are used to refine surgical context data, which in turn improves subsequent element identification and annotation accuracy. The feedback is optimized to update only the necessary portions of context data rather than reprocessing all data, maintaining high annotation accuracy while preserving processing speed through selective updates.
Solution Approach 2:
Context data refinement is performed periodically at strategically determined intervals rather than continuously for every frame. This periodic refinement maintains high annotation accuracy for critical elements while allowing the system to process frames at higher speeds between refinement cycles, balancing accuracy and productivity.
Data Source
AI summary
Systems, methods, and instrumentalities are disclosed for identification of image shapes based on situational awareness of a surgical image and annotation of shapes or pixels. A surgical video associated with a surgical procedure may be obtained. Surgical context data for the surgical procedure may be obtained. Elements in the video frames may be identified based on the surgical context data using image processing. Annotation data may be determined and generated for the video frames, for example, based on the surgical context data and the identified element(s).